2023 Seminar Archive

Controlling Populations of Neural Oscillators

Jeff Moehlis

Professor and Chair, Department of Mechanical Engineering
University of California at Santa Barbara
Santa Barbara, CA

Many challenging problems that consider the analysis and control of neural brain rhythms have been motivated by the advent of deep brain stimulation as a therapeutic treatment for a wide variety of neurological disorders. In a computational setting, neural rhythms are often modeled using large populations of coupled, conductance-based neurons. Control of such models comes with a long list of challenges: the underlying dynamics are nonnegligibly nonlinear, high dimensional, and subject to noise; hardware and biological limitations place restrictive constraints on allowable inputs; direct measurement of system observables is generally limited; and the resulting systems are typically highly underactuated. In this talk, I highlight a collection of recent analysis techniques and control frameworks that have been developed to contend with these difficulties. Particular emphasis is placed on the problem of desynchronization for a population of pathologically synchronized neural oscillators, a problem that is motivated by applications to Parkinson's disease where pathological synchronization is thought to contribute to the associated motor control symptoms.

Jeff Moehlis received a Ph.D. in Physics from UC Berkeley in 2000, and was a Postdoctoral Researcher in the Program in Applied and Computational Mathematics at Princeton University from 2000-2003. He joined the Department of Mechanical Engineering at UC Santa Barbara in 2003, and is currently Chair of this department. He was also recently the Chair of the Program in Dynamical Neuroscience at UC Santa Barbara. He has been a recipient of a Sloan Research Fellowship in Mathematics and a National Science Foundation CAREER Award, and was Program Director of the SIAM Activity Group in Dynamical Systems from 2008-2009. Jeff's current research includes applications of dynamical systems and control techniques to neuroscience, cardiac dynamics, and collective behavior. He has published over 100 journal / conference proceedings articles on these and other topics including shear flow turbulence, microelectromechanical systems, energy harvesting, and dynamical systems with symmetry.

Wednesday, January 18, 2023

3:30 PM

Stauffer Science Lecture Hall, Room 102 (SLH 102)

The Zoom webinar is at https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09.

host: Nguyen

Machine Learning for Scientific Discovery, with Examples in Fluid Mechanics

Steven L. Brunton

Professor of Mechanical Engineering
Department of Mechanical Engineering
University of Washington
Seattle, WA

Accurate and efficient nonlinear dynamical systems models are essential to understand, predict, estimate, and control complex natural and engineered systems. In this talk, I will explore how machine learning may be used to develop these models purely from measurement data. We explore the sparse identification of nonlinear dynamics (SINDy) algorithm, which identifies a minimal dynamical system model that balances model complexity with accuracy, avoiding overfitting. This approach tends to promote models that are interpretable and generalizable, capturing the essential “physics” of the system. We also discuss the importance of learning effective coordinate systems in which the dynamics may be expected to be sparse. This sparse modeling approach will be demonstrated on a range of challenging modeling problems, for example in fluid dynamics. Because fluid dynamics is central to transportation, health, and defense systems, we will emphasize the importance of machine learning solutions that are interpretable, explainable, generalizable, and that respect known physics.

Steven L. Brunton is a Professor of Mechanical Engineering at the University of Washington. He is also Adjunct Professor of Applied Mathematics and Computer science, and a Data Science Fellow at the eScience Institute. Steve received the B.S. in mathematics from Caltech in 2006 and the Ph.D. in mechanical and aerospace engineering from Princeton in 2012. His research combines machine learning with dynamical systems to model and control systems in fluid dynamics, biolocomotion, optics, energy systems, and manufacturing. He received the Army and Air Force Young Investigator Program (YIP) awards and the Presidential Early Career Award for Scientists and Engineers (PECASE). Steve is also passionate about teaching math to engineers as co-author of three textbooks and through his popular YouTube channel, under the moniker “eigensteve”.

Wednesday, January 25, 2023

3:30 PM

Stauffer Science Lecture Hall, Room 102 (SLH 102)

The Zoom webinar is at https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09.

host: Nguyen

Dynamical Weighs: Learning Smooth Latent-Dynamics for Advection-Dominated Systems via Consistency-Constrained Hyper-Networks

Leonardo Zepeda-Núñez

Senior Research Scientist
Google Research
and
Assistant Professor
Department of Mathematics
University of Wisconsin-Madison
Madison, Wisconsin

We present a data-driven, space-time continuous framework to learn surrogate models for complex physical systems described by partial differential equations (PDEs). Our approach involves constructing hypernetwork-based latent dynamical models directly on the parameter space of a compact representation network specially tailored to the state space of the target system. The framework leverages the expressive power of the network with a specially designed consistency-inducing regularization to obtain latent trajectories that are both low-dimensional and smooth. These properties render our surrogate models highly efficient at inference time.

We demonstrate the effectiveness of our approach on advection-dominated systems. These systems have slow-decaying Kolmogorov n-widths that hinders standard methods, including reduced order modeling, from producing high-fidelity simulations at low cost. We show that our method is able to generate accurate multi-step rollout predictions at high efficiency, for several one- and two-dimensional PDEs. The resulting rollouts are shown to be stable and reflect statistics that are consistent with the ground truths.

Leonardo Zepeda-Núñez is a Senior Research Scientist at Google Research and an Assistant Professor of Mathematics at the University of Wisconsin-Madison. He has held postdoctoral positions at Lawrence Berkeley Lab and University of California, working with Lin Lin and Hongkai Zhao respectively. He received a Ph.D. in Mathematics from MIT in 2015 under the direction of Laurent Demanet, an M.Sc. from University of Paris VI in 2010, and a Diploma from École Polytechnique in 2009. His research emcompases scientific machine learning with applications to weather and climate, electronic structure computations, wave-based inverse problems, and fast PDE solvers for wave phenomena.

Wednesday, February 8, 2023

3:30 PM

Stauffer Science Lecture Hall, Room 102 (SLH 102)

The Zoom webinar is at https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09.

host: Ronney

Strategies to Achieve Order: Colloidal Self-Assembly and Nano-Enhanced Additive Manufacturing

David Doan

Mechanical Engineering Department
Stanford University
Stanford, CA

Achieving order is key to the improvement of materials properties in applications such as mechanics, catalysis, and photonics. Colloidal self-assembly has been a field of interest due to its ability to manipulate nanoscale/microscale particles to create periodic structures. However, a challenge in this field is the ability to expand the possible phase space of crystal structures that can be formed. Here, we explore the fundamentals of shape- or entropy-driven self-assembly to achieve different types of order. I will discuss an experimental framework that allows us to fabricate particles of complex shapes using two-photon lithography and assemble them under a gravitational field. I will present experimental, analytical, and computational results for the self-assembly of truncated tetrahedrons on a 2D interface.

I will also present on enhancing mechanical properties through the addition of atomically precise nanoclusters in polymeric structures to create nanocomposites. This, in conjunction with two-photon lithography, allows us to fabricate strong but lightweight structures of arbitrary shapes. We show that these nanoclusters enhance the overall mechanical properties of the structure, above what is expected from simple composite theory.

David Doan is currently a PhD candidate in Mechanical Engineering at Stanford University under the supervision of Professor Wendy Gu, with a planned graduation in mid-2023. He received his Masters’ degree in Mechanical Engineering at Stanford and Bachelors’ degree in Mechanical Engineering at MIT. He is an NSF Graduate Fellow and Questbridge Scholar. His current research focuses on the fundamentals of self-assembly and mechanics but eventually wants to develop more scalable fabrication techniques that connect the nanoscale to the macroscale.

Wednesday, February 22, 2023

3:30 PM

Stauffer Science Lecture Hall, Room 102 (SLH 102)

The Zoom webinar is at https://usc.zoom.us/j/98775609685?pwd=a2lSd01oY0o2KzA4VWphbGxjWk5Qdz09.

host: Ronney

Humanitarian Engineering: Educating Bold, Responsible, and Innovative Leaders

Janet Ellzey

Professor and Engineering Foundation Centennial Teaching Fellow
Walker Department of Mechanical Engineering
University of Texas at Austin
Austin, TX

Humanitarian engineering, the application of engineering solutions to low-income or marginalized communities, is a growing field in the US and worldwide. Sometimes called development engineering, researchers and practitioners focus on culturally appropriate solutions for resource-constrained environments such as refugee camps or low-income communities. Engineering schools are increasingly recognizing the importance of training students in humanitarian engineering and are developing programs using different approaches, from student organizations to full degree programs. At the University of Texas at Austin, Dr. Janet Ellzey has built an academic certificate that provides students with several pathways to use their engineering skills to positively impact the world, including a design and build program in which student teams partner with local communities and an innovation program to develop new technologies for the International Federation of Red Cross and Red Crescent Societies. In this talk, Dr. Ellzey will discuss this exciting engineering field, describe the programs at UT-Austin with data on the diversity of students enrolled in the program, and present challenges and opportunities for universities wanting to enter this field.

Janet Ellzey is a professor of mechanical engineering and the Engineering Foundation Centennial Teaching Fellow in the Cockrell School of Engineering at the University of Texas at Austin. She received her BS and MS degrees in Mechanical Engineering from The University of Texas at Austin and her PhD from the University of California-Berkeley. After more than 30 years of conducting experimental and computational research in the field of combustion, she pivoted her career to focus on expanding unique educational opportunities for undergraduate students. Recognizing the enthusiasm that the current generation of students has for social justice, she launched a program in humanitarian engineering which she now directs. Through creative partnerships with local communities abroad as well as with major international organizations, she has developed a network to educate the next generation of leaders while positively impacting the world.

Monday, February 27, 2023

3:30 PM

Stauffer Science Lecture Hall, Room 102 (SLH 102)

The Zoom webinar is at https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09.

host: Ronney

Fracture of Highly Entangled Polymer Networks

Junsoo Kim

Postdoctoral Researcher
John A. Paulson School of Engineering and Applied Sciences
Harvard University
Cambridge, MA

Polymers pollute our planet. Part of this pollution comes from tires. Every year, 0.8 kg of rubber particles are shed by tires per capita in the world.¹ A recent study showed that rainstorms wash the rubber particles into rivers, where toxic chemical compounds leach out and kill fish.² Despite its significant impact on the environment, the development of rubbers resistant to fracture has been stagnant for decades. In this talk, I will discuss how to improve the fracture properties of polymer networks, such as rubbers and gels. The key idea is that entanglements stiffen polymers but do not embrittle them, whereas crosslinks stiffen polymers and embrittle them (i.e., stiffness-toughness conflict). Therefore, highly entangled polymer networks in which entanglements greatly outnumber crosslinks can be both stiff and tough. Furthermore, whereas traditional toughening mechanisms are based on sacrificial bonds causing hysteresis and fatigue, highly entangled polymer networks achieve high toughness by stress deconcentration, leading to high strength, elasticity, and fatigue resistance. This toughening mechanism is based on the polymer topology, not chemistry, so it is generally applicable to many other polymer systems, such as various monomers, preexisting polymers,⁴ and filled rubbers.⁵ It is hoped that this work will reactivate the development of wear-resistant tires. Such materials can also be explored in other high-volume applications such as dampers and belts, as well as emerging applications such as soft robots, wearable devices, tissue replacements, bioprinting, and humanoids.

¹ P. J. Kole, A. J. Löhr, F. G. A. J. V. Belleghem, A. M. J. Ragas, Int. J. Environ. Res. Public Health, 14(10), 1265 (2017)
² Z. Tian et. al., Science, 371(6525), 185-189 (2020)
³ J. Kim*, G. Zhang*, M. Shi, Z. Suo, Science, 374(6564), 212-216 (2021)
⁴ G. Nian*, J. Kim*, X. Bao, Z. Suo, Adv. Mat., 34(50), 2206577 (2022)
⁵ J. Steck*, J. Kim*, Y. Kutsovsky, Z. Suo, under review

Junsoo Kim is a postdoctoral researcher at the John A. Paulson School of Engineering and Applied Sciences, Harvard University. He earned his Ph.D. in the Material Science and Mechanical Engineering department at Harvard University in 2022, where he studied fracture of soft materials. Before joining Harvard in 2017, he was a researcher at Electronics Telecommunications Research Institute since 2014. He earned his M.S. in 2013 and B.S. in 2011 at Seoul National University in South Korea. He co-authored 31 papers in peer-reviewed journals, registered six patents, and received fellowships, including the Ilun Science and Technology Foundation (2013) and Kwanjeong Educational Foundation (2017).

Wednesday, March 1, 2023

3:30 PM

Stauffer Science Lecture Hall, Room 102 (SLH 102)

The Zoom webinar is at https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09.

host: Ronney

Additive Manufacturing of Emerging Complex Alloys with Engineered Structures

Wen Chen

Assistant Professor
Department of Mechanical and Industrial Engineering
University of Massachusetts Amherst
Amherst, MA

The increasing demands for materials serving under extreme environments call for the development of emerging classes of metal alloys with increasingly complex compositions. However, synthesis and processing of complex alloys via traditional routes are challenging. Additive manufacturing, also called 3D printing, is a disruptive technology for creating materials and components in a single print. Harnessing the vast compositional space of complex alloys and the far-from-equilibrium processing conditions (e.g., large thermal gradients and high cooling rates) of additive manufacturing provides a paradigm-shifting pathway for material design. In this talk, I will present the potential of utilizing laser additive manufacturing and direct ink writing to produce metal alloys with engineered structural hierarchy across multiple length scales. These unique microstructures give rise to exceptional mechanical and functional properties that extend far beyond those accessible by conventional manufacturing. In addition, I will discuss the abundant opportunities enabled by additive manufacturing for high-throughput materials discovery to accelerate the pace of future materials search for a wide range of applications in aerospace, biomedical, and renewable energy.

Wen Chen is an Assistant Professor in the Department of Mechanical and Industrial Engineering at University of Massachusetts Amherst. He completed his Ph.D. degree in Mechanical Engineering and Materials Science at Yale University in 2016. After his Ph.D., he worked as a postdoctoral research scientist at Lawrence Livermore National Laboratory, where he studied a variety of additive manufacturing techniques such as projection stereolithography, direct ink writing, and laser powder bed fusion. Dr. Chen's current research interests include advanced manufacturing, mechanical behavior of materials, physical metallurgy, and architected materials. He is the recipient of several prestigious awards including the SME Outstanding Young Manufacturing Engineer Award and NSF CAREER Award. He has served as an editorial board member of Scientific Reports since 2018.

Monday, March 6, 2023

3:30 PM

Stauffer Science Lecture Hall, Room 102 (SLH 102)

The Zoom webinar is at https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09.

host: Ronney

Battery Avatar: First-Principles Modeling and Data Analytics

Weiyu Li

PhD Student
Stanford University
Stanford, CA

Rechargeable lithium batteries are electrochemical devices that are widely used in portable electronics and electric-powered vehicles. A breakthrough in battery performance requires advancements in battery cell configurations at the microscale level. This, in turn, places a premium on the ability to accurately predict complex multiphase thermo-electrochemical phenomena, e.g., migration of ions interacting with composite porous materials that constitute a battery cell microstructure. Optimal design of porous cathodes requires efficient quantitative models of microscopic (pore-scale) electrochemical processes and their impact on battery performance. In this talk, I will discuss effective properties (electrical conductivity, ionic diffusivity, reaction parameters) of a composite electrode comprising the active material coated with a mixture of the binder and conductor (the carbon binder domain or CBD). When used to parameterize the industry-standard pseudo-two-dimensional (P2D) models, they significantly improve the predictions of lithiation curves in the presence of CBD. On the lithium anode, dendritic growth is a leading cause of degradation and catastrophic failure of lithium-metal batteries. Deep understanding of this phenomenon would facilitate the design of strategies to reduce, or completely suppress, the instabilities characterizing electrodeposition on the lithium anode. This would improve the safety of lithium-metal batteries with liquid electrolyte and all-solid-state lithium batteries. I will present the results of our analysis, which indicate that the use of anisotropic electrolytes and buffer layers can suppress dendritic growth of lithium metal.

Weiyu Li has received her M.Sc. degree in Mechanical and Aerospace Engineering from Princeton University and is scheduled to obtain her PhD in Energy Science and Engineering from Stanford University in the Spring of 2023. Her research focuses on modeling and simulation of electrochemical transport in energy storage systems, aiming to provide mechanistic insights into the optimal design of porous electrodes, electrolyte, etc. Her other research interests include data assimilation and biomedical modeling. Weiyu Li is the recipient of the Siebel Scholars Award in Energy Science, class of 2023, and of the Princeton University Fellowship in Natural Sciences and Engineering.

Monday, March 20, 2023

3:30 PM

Laufer Conference Room (OHE 406)

The Zoom webinar is at https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09.

host: Ronney

Finding Order in Disorder: Atomic-Scale Understanding of Phase Transformations

Dennis Kim

Research Scientist
University of California Los Angeles
Los Angeles, CA

Crystalline imperfections and their dynamics are essential in phase transformations and structure-property relationships in materials. Classical methods for determining atomic structures average over many unit cells. As a result, such methods cannot correctly capture atomic-level information on amorphous packing, point defects, chemical ordering, strain, and interfaces. I will first present my recent work extending atomic electron tomography (AET) to overcome the limitations of conventional methods to obtain 3D atomic packing information with picometer precision in amorphous materials. With every atom accounted for, we can understand how atoms in amorphous solids arrange in short- to medium-range order and the implications of these findings for metallic glasses. I will then discuss other systems where chemical ordering and crystalline imperfections of point defects, strain, and interfaces play an essential role in phase transformations and atomic-scale structure-property relationships. I will also present recent efforts in developing an electron thermal diffuse scattering method to determine spatially resolved lattice dynamics. The diffuse patterns are highly sensitive to differences in phonon energies. Combining high-reciprocal space sampling and high-dynamic-range imaging methods, and machine-learned interatomic potential-based dynamical simulations, we are able to observe temperature-dependent soft phonon mode dynamics and nuclear quantum effects. These findings have far-reaching implications in understanding heat transport. Finally, I will show how feedback loops powered by experimental coordinates with picometer accuracy, scattering spectroscopy, and ab initio computational methods will guide future materials discovery and design.

Dennis Kim is a research scientist at the University of California Los Angeles and holds a PhD in Materials Science from the California Institute of Technology. Prior to his current position, he was a postdoctoral associate in the Department of Materials Science and Engineering at the Massachusetts Institute of Technology and a STROBE postdoctoral fellow in the Department of Physics and Astronomy at the University of California Los Angeles. His research background is in materials thermodynamics and understanding phase transformations through state-of-the-art scattering, imaging, and quantum mechanical computational techniques. He is interested in developing and optimizing materials for various applications in thermal, energy, and quantum sciences through a fundamental understanding from the atom up.

Monday, March 22, 2023

3:30 PM

Stauffer Science Lecture Hall, Room 102 (SLH 102)

The Zoom webinar is at https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09.

host: Nguyen

Recent Progress on Singularity Formation of 3D Incompressible Euler and Navier-Stokes Equations

Thomas Hou

Charles Lee Powell professor of Applied and Computational Mathematics
Caltech
Pasadena, CA

Whether the 3D incompressible Euler and Navier equations can develop a finite time singularity from smooth initial data is one of the most challenging problems in fluid dynamics. In this talk, I will present a recent result with Dr. Jiajie Chen in which we prove finite time blowup of the 2D Boussinesq and 3D Euler equations with smooth initial data. There are several essential difficulties in establishing such blowup result. We overcome these difficulties by decomposing the solution operator into a leading order operator that enjoys sharp stability estimates plus a finite rank perturbation operator that can be estimated by using computer assisted proof. This enables us to establish nonlinear stability of the approximate self-similar profile and prove nearly self-similar blowup of the 2D Boussinesq and 3D Euler equations. I will also report some recent progress on potentially singular behavior of the 3D incompressible Navier-Stokes equations.

Thomas Yizhao Hou is the Charles Lee Powell professor of applied and computational mathematics at Caltech. His research interests include 3D Euler singularity, interfacial flows, multiscale problems, and adaptive data analysis. He received his Ph.D. from UCLA in 1987, and became a tenure track assistant professor at the Courant Institute in 1989, and a tenured associate professor in 1992. He moved to Caltech in 1993 and was named the Charles Lee Powell Professor in 2004. Dr. Hou has received a number of honors and awards, including Fellow of American Academy of Arts and Sciences in 2011, a member of the inaugural class of SIAM Fellows in 2009 and AMS Fellows in 2012, the SIAM Ralph E. Kleinman Prize in 2023, the SIAM Outstanding Paper Prize in 2018, the SIAM Review SIGEST Award in 2019, the Computational and Applied Sciences Award from USACM in 2005, the Morningside Gold Medal in Applied Mathematics in 2004, the SIAM Wilkinson Prize in Numerical Analysis and Scientific Computing in 2001, the Frenkiel Award from the Division of Fluid Mechanics of American Physical Society in 1998, the Feng Kang Prize in Scientific Computing in 1997, a Sloan fellow from 1990 to 1992. He was also the founding Editor-in-Chief of the SIAM Journal on Multiscale Modeling and Simulation from 2002 to 2007.

Wednesday, March 29, 2023

3:30 PM

Stauffer Science Lecture Hall, Room 102 (SLH 102)

The Zoom webinar is at https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09.

host: Pahlevan

Can't Touch This: Real-Time, Provably Safe Motion Planning and Control for High Dimensional Autonomous Systems

Ram Vasudevan

Associate Professor
Mechanical Engineering and Robotics Departments
University of Michigan
Ann Arbor, MI

A key challenge to the widespread deployment of robotic manipulators is the need to ensure safety in arbitrary environments while generating new motion plans in real-time. This talk describes a technique that constructs a parameterized representation of the forward reachable set that it then uses in concert with predictions to enable certified, collision checking. To improve computational speed, this talk describes how to represent this parameterized reachable set using a neural implicit representation without sacrificing any safety guarantees. This approach, which is guaranteed to generate safe behavior, is demonstrated across a variety of different real-world platforms including ground vehicles, manipulators, and walking robots.

Ram Vasudevan is an associate professor in the Mechanical Engineering and Robotics Departments at the University of Michigan. He received a BS in Electrical Engineering and Computer Sciences, an MS degree in Electrical Engineering, and a PhD in Electrical Engineering all from the University of California, Berkeley. He is a recipient of the NSF CAREER Award, the ONR Young Investigator Award, and the 1938E Award from the University of Michigan. His work has received best paper awards at the IEEE Conference on Robotics and Automation, the ASME Dynamics Systems and Controls Conference, and IEEE International Conference on Biomedical Robotics and Biomechatronics, and has been finalist for best paper at Robotics: Science and Systems.

Wednesday, April 5, 2023

3:30 PM

Stauffer Science Lecture Hall, Room 102 (SLH 102)

The Zoom webinar is at https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09.

host: Nguyen

Large Amplitude Internal Wave Transformation Between 500m and the Surfzone

Kristen Davis

Associate Professor
Civil & Environmental Engineering & Earth System Science
University of California, Irvine
Irvine, CA

Internal waves strongly influence the physical and chemical environment of coastal ecosystems worldwide. We report novel observations from a dense and rapidly-sampling array spanning depths from 500 m to shore near Dongsha Atoll in the South China Sea to track large amplitude internal solitary wave (ISW) shoaling, breaking, and runup. During the observational period incident ISW amplitudes ranged between 78 m and 146 m with propagation speeds between 1.40 m/s and 2.38 m/s. Fissioning ISWs generated larger trailing elevation waves when the thermocline was deep, and evolved into onshore propagating bores in depths near 100 m. Collapsing ISWs contained significant mixing and reduced upslope bore propagation. Bores on the shallow forereef drove bottom temperature variation in excess of 10 degrees Celsius and near-bottom cross-shore currents in excess of 0.4 m/s. Bores decelerated upslope, consistent with upslope two-layer gravity current theory, though runup extent, Xr, was offshore of the predicted gravity current location. Background stratification affected the bore runup, with Xr farther offshore when the Korteweg-de Vries nonlinearity coefficient, α, was negative. Fronts associated with the shoaling local internal tide, but equal in magnitude to the soliton-generated bores, were observed onshore of 20 m depth.

Kristen Davis is an Associate Professor of Civil & Environmental Engineering at the University of California, Irvine. She is an engineer and oceanographer who is interested in studying how physical processes shape coastal waters – combining principles of fluid mechanics, oceanography, and ecology. Kristen uses both field observations and numerical tools to examine circulation in the ocean, its natural variability, and influence on marine ecosystems and human-nature interactions. Kristen earned a Ph.D. in Civil & Environmental Engineering at Stanford University in 2009 and was a postdoctoral researcher at the Woods Hole Oceanographic Institution and the Applied Physics Laboratory at the University of Washington. Her recent research is focused on understanding nonlinear internal wave dynamics and the feasibility of the large-scale, offshore cultivation of macroalgae for the production of biofuels and as a strategy to sequester carbon dioxide.

Wednesday, April 12, 2023

3:30 PM

Stauffer Science Lecture Hall, Room 102 (SLH 102)

The Zoom webinar is at https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09.

host: Spedding

Interactions of Shock Waves and Turbulence Through Numerical Simulations

Ivan Bermejo-Moreno

Assistant Professor
Department of Aerospace & Mechanical Engineering
USC
Los Angeles, CA

Hypersonic flight and propulsion pose fundamental challenges that arise from interactions between shock waves and turbulence. These interactions can be beneficial, enhancing the mixing of fuel and oxidizer in a scramjet engine, but they can also be detrimental, compromising the integrity of the flying vehicle through uncontrolled aerothermostructural coupling. This presentation will highlight recent developments on the prediction and understanding of these phenomena by means of high-fidelity numerical simulations. First, focus will be placed on interactions of shock waves reflecting off turbulent boundary layers that develop along rigid and flexible walls, by loosely coupling a wall-modeled large-eddy simulation solver for the fluid flow with an elastic solid structural solver that accounts for geometric nonlinearities. Strong shock/boundary-layer interactions will be emphasized, resulting in mean flow separation and low-frequency unsteadiness that can couple with natural frequencies of the solid structure. Simulation results will be compared with supersonic wind-tunnel experiments. Second, the enhancement of scalar mixing under canonical shock-turbulence interactions will be addressed by means of shock-capturing direct numerical simulations, evaluating the effects of the shock and turbulence Mach numbers, and the Reynolds number. Statistical analyses will highlight changes along the mean flow direction of scalar variance and dissipation-rate budgets, flow topology, and alignments of the scalar gradient with vorticity and strain-rate eigendirections. A novel methodology to track the time evolution of geometric and physical quantities of turbulent flow structures will be introduced and applied to study the dynamics of isoscalar surfaces across the shock-turbulence interaction.

Ivan Bermejo-Moreno received an engineer's degree from the School of Aeronautics at the Polytechnic University of Madrid, Spain (2001). He then worked for two years in the aerospace industry (GMV) and received a Fulbright Fellowship to pursue M.Sc. (2004) and Ph.D. (2008) degrees in aeronautics from the California Institute of Technology. Afterwards, he held a postdoctoral research fellowship at the Center for Turbulence Research, Stanford University/NASA Ames Research Center (2009-2014). He joined the Aerospace and Mechanical Engineering Department at the University of Southern California as assistant professor in 2015. His research combines numerical methods, physical modeling, and high-performance computing for the simulation and analysis of turbulent fluid flows involving multi-physics phenomena. He is a recipient of the Rolf D. Buhler Memorial Award, the William F. Ballhaus Prize, the Hans G. Hornung Prize, and the NSF CAREER Award.

Wednesday, April 19, 2023

3:30 PM

Stauffer Science Lecture Hall, Room 102 (SLH 102)

The Zoom webinar is at https://usc.zoom.us/j/95805178776?pwd=aEtTRnQ2MmJ6UWE4dk9UMG9GdENLQT09.

Flexoelectricity and Electrets

Pradeep Sharma

Hugh Roy and Lillie Cranz Cullen Distinguished University Professor
and
Chair of Mechanical Engineering
Department of Mechanical Engineering
University of Houston
Houston, TX

The ability of certain materials to convert electrical stimuli into mechanical deformation, and vice versa, is a prized property. Not surprisingly, applications of such so-called piezoelectric materials are broad—ranging from energy harvesting to self-powered sensors. In this presentation, I will highlight a relatively understudied electromechanical coupling called flexoelectricity that appears to have implications in topics ranging from biophysics to the design of next-generation soft multifunctional materials. Specifically, I will argue, through computational examples, the tantalizing possibility of creating “apparently piezoelectric” materials without piezoelectric materials—e.g. graphene, emergence of “giant” piezoelectricity at the nanoscale, and (among others) the mechanisms underpinning magnetoreception in certain animals.

Pradeep Sharma is the Hugh Roy and Lillie Cranz Cullen Distinguished University Professor and Chair of Mechanical Engineering at the University of Houston. He also has a joint appointment in the Department of Physics. He received his Ph.D. in mechanical engineering from the University of Maryland at College Park in the year 2000. Subsequent to his doctoral degree, he was employed at General Electric R & D for more than three years as a research scientist. He joined the department of mechanical engineering at University of Houston in January 2004. He is a member of the US National Academy of Engineering. His other honors and awards include the Young Investigators Award from Office of Naval Research, Thomas J.R. Hughes Young Investigator Award from the ASME, Texas Space Grants Consortium New Investigators Program Award, the Fulbright fellowship, the Melville medal, the James R. Rice medal from the Society of Engineering Science, ASME Charles R. Russ medal, the Guggenheim, and the University of Houston Research Excellence Award. He is a fellow of the ASME, the associate editor of the Journal of the Mechanics and Physics of Solids, chief-editor of the Journal of Applied Mechanics and serves on the editorial board of several other journals. He specializes in the broadly defined fields of continuum mechanics of solids and theoretical and computational materials science.

Wednesday, August 23, 2023

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/96742044706?pwd=RDFlZ0tTYTdXVXhDeGgxSElJT3BzUT09.

host: Plucinsky

Thin-Film Flows: From Similarity Solutions to New Insights in Molecular Biology

Howard Stone

Donald R. Dixon ’69 and Elizabeth W. Dixon Professor in Mechanical and Aerospace Engineering
Department of Mechanical and Aerospace Engineering
Princeton University
Princeton, NJ

Fluid mechanics has a rich history, as of course does mechanics more generally. The ideas bridge science and engineering disciplines, even as they generate new fundamental research questions in fluid mechanics. In this talk I sketch some recent themes* from my research group, which bridge a wide range of length scales. First, I give a brief survey of some of the fluid mechanics problems that we have been investigating in recent years. Second, whereas traditional similarity solutions in course work and research typically involve nonlinear equations with two independent variables, I will illustrate an experimentally motivated similarity solution involving three independent variables, for which we construct an analytical solution that can be compared with experimental measurements. Third, I discuss the formation of the spindle in a dividing cell, which is a fundamental aspect of molecular biology. Experiments documenting a condensed protein phase on growing microtubules are reported, followed by the appearance of the Rayleigh-Plateau instability, which produces discrete droplets along a microtubule: the drops drive branching nucleation, which is an important mechanism for the developing spindle.

*The research described was performed by many people in my research group, as well as some external collaborations.

Howard Stone received the B.S. degree in Chemical Engineering from UC Davis in 1982 and the PhD in Chemical Engineering from Caltech in 1988. Following a postdoctoral fellowship at the University of Cambridge, in 1989 Howard joined the faculty of the (now) School of Engineering and Applied Sciences at Harvard University, where he eventually became the Vicky Joseph Professor of Engineering and Applied Mathematics. In July 2009 Howard moved to Princeton University where he is Donald R. Dixon ’69 and Elizabeth W. Dixon Professor in Mechanical and Aerospace Engineering.

Professor Stone's research interests are in fluid dynamics, especially as they arise in research and applications at the interface of engineering, chemistry, physics, and biology. He is a Fellow of the American Physical Society (APS), and is past Chair of the Division of Fluid Dynamics of the APS. Currently he is on the editorial or advisory boards of Physical Review Fluids, Langmuir, and Soft Matter, and is co-editor of the Soft Matter Book Series. He is the first recipient of the G.K. Batchelor Prize in Fluid Dynamics (2008) and in 2016 recipient of the Fluid Dynamics Prize of the APS. He was elected to the National Academy of Engineering in 2009, the American Academy of Arts and Sciences in 2011, the National Academy of Sciences in 2014, the Royal Society (United Kingdom) as a Foreign Member in 2022, and the American Philosophical Society in 2022.

Wednesday, August 30, 2023

Location: The Franklin Suite
(Tutor Campus Center, 3rd Floor)

Reception will begin promptly at 12 noon.

Seminar immediately after the reception.

The Zoom webinar is at https://usc.zoom.us/j/96742044706?pwd=RDFlZ0tTYTdXVXhDeGgxSElJT3BzUT09.

host: Ronney

From Autonomous Systems to Generative AI and Backwards: An Optimal Control Perspective

Evangelos Theodorou

Associate Professor
and
Amazon CORE-AI Scholar

Guggenheim School of Aerospace Engineering
Georgia Institute of Technology
Atlanta, GA

In this talk, I will present use cases for stochastic optimal control that span the areas of terrestrial high-speed navigation, control of VTOL vehicles for urban aerial mobility, large-scale multi-agent control, training of score-based models in generative AI, opinion dynamics depolarization and single cell RNA sequencing. The presentation will cover fundamental concepts in stochastic control theory that includes path integrals, Forward-Backward SDEs and Schrodinger Bridges, and highlight the importance of scalable optimization for large scale and real time decision-making. In the last part of the talk, we will look into different distributed optimization architectures for large-scale optimal control and conclude with future directions and vision at the intersection of autonomy, generative AI and large-scale optimization.

Evangelos A. Theodorou is an Associate professor with the Guggenheim School of aerospace engineering at Georgia Institute of Technology and Amazon CORE-AI Scholar. He is also the director of the Autonomous Control and Decision Systems Laboratory, and he is affiliated with the Institute of Robotics and Intelligent Machines and the Center for Machine Learning Research at Georgia Tech. Evangelos Theodorou holds a BS in Electrical Engineering, from the Technical University of Crete (TUC), Greece in 2001. He has also received a MSc in Production Engineering from TUC in 2003, a MSc in Computer Science and Engineering from University of Minnesota in spring of 2007 and a MSc in Electrical Engineering on dynamics and controls from the University of Southern California(USC) in Spring 2010. In May of 2011 he graduated with his PhD, in Computer Science from USC. After his PhD, he was a Postdoctoral Research Fellow with the department of computer science and engineering, University of Washington, Seattle. Evangelos Theodorou is the recipient of the King-Sun Fu best paper award of the IEEE Transactions on Robotics in 2012 and a recipient several best paper awards and nominations in top conferences in machine learning and robotics communities. His theoretical research spans the areas of stochastic optimal control theory, machine learning and optimization. Applications involve learning, planning and control in autonomous, robotic and aerospace vehicles, multi-agent and large-scale systems, and dynamical systems in social sciences and biology.

Wednesday, September 6, 2023

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/98121141178?pwd=VGEyaXVWYnRaazFYWUVhbVAycGVWQT09.

host: Nguyen

Hemodynamic Mechanisms of Heart-Aorta-Brain Coupling: From Fluid Dynamics to Advanced Diagnostics and Therapeutics

Niema Pahlevan

Assistant Professor
Department of Aerospace & Mechanical Engineering
University of Southern California
Los Angeles, CA

The circulatory system operates based on a delicate hemodynamic balance between the heart, the aorta (the largest vessel that extends from the heart), and major physiological organs, such as the brain and kidneys. Optimal hemodynamic coupling between these organs can be impaired due to aging and/or diseases. Therefore, understanding the complex fluid dynamic coupling (hemodynamic interactions) between the heart, aorta, and brain is crucial for the diagnostics and therapeutics of related diseases, such as heart failure (HF), dementia, and myocardial infarction (heart attack). The general hypothesis is that wave dynamics in the aorta dominate the pulsatile hemodynamics of the heart and brain, as well as the nonlinear interaction between the two. This talk will elucidate the systems-level effects of aortic hemodynamics on heart-brain interactions. Our long-term goal is to transform our findings into the development of diagnostic, therapeutic, and monitoring devices for heart diseases and vascular brain damage.

Niema Pahlevan's research brings physics and mathematics tools to the study of biological systems ranging from understanding the biomechanics of cardiovascular and cerebrovascular diseases to modeling physiological systems. His ultimate goal in each research initiative is to use mathematical and mechanical engineering principles to establish new techniques and/or devices that are relevant for practicing clinicians. Dr. Pahlevan completed his undergraduate degree in Mechanical Engineering from the University of Tehran, his M.S. in Mechanical Engineering from the California State University Northridge, and PhD in Bioengineering from the California Institute of Technology (Caltech). He completed his postdoctoral training in hemodynamics and cardiovascular imaging at Caltech and Huntington Medical Research Institutes (HMRI) as a Boswell fellow. Dr. Pahlevan joined USC in 2017 as an Assistant Professor of Aerospace and Mechanical Engineering in the Viterbi School of Engineering and Assistant Professor of Medicine in the Division of Cardiovascular Medicine. He is the recipient of both the NSF CAREER Award and the American Heart Association’s Career Development Award. In 2022, he received the Junior Research Award from USC's Viterbi School of Engineering and was also appointed the holder of the Gordon S. Marshall Early Career Chair in Engineering.

Wednesday, September 13, 2023

3:30 PM

Seaver Science Library, Room 202 (SSL 102)

The Zoom webinar is at https://usc.zoom.us/j/98121141178?pwd=VGEyaXVWYnRaazFYWUVhbVAycGVWQT09.

host: Ronney

Two Fundamental Relations for Turbulent Flows

Perry Johnson

Assistant Professor
Mechanical and Aerospace Engineering department
University of California, Irvine
Irvine, CA

Two fundamental effects of turbulence are (a) an increased rate at which kinetic energy is dissipated into heat and (b) an enhanced momentum (and heat) flux across boundary layers leading to much higher skin friction drag forces (and surface heat transfer). This presentation will introduce and apply two exact relations related to these fundamental effects, respectively. First, the concept of Stokes Flow Regularization (SFR) provides an exact expression for the scale-wise energy cascade rate in terms of vortex stretching and strain-rate self-amplification. Applied to data from fully-resolved simulations, this precisely quantifies the mechanisms responsible for generating large dissipation rates at small scales. SFR also serves as an intriguing alternative to spatial filtering as the basis of large-eddy simulation modeling. Specific potential modeling advantages will be discussed. Second, the Angular Momentum Integral (AMI) equation for turbulent boundary layers will be introduced. The AMI equation quantifies the impact of various flow phenomena throughout a boundary layer flow on the skin friction relative to a baseline laminar flow. An analogous integral equation for the surface heat transfer will also be introduced. Together, these provide a powerful method for probing flow data in terms of key engineering quantities of interest. Example applications for AMI-based analysis will be shown for boundary layer transition and supersonic turbulent boundary layers.

Perry Johnson Perry Johnson earned his Ph.D. in 2017 from Johns Hopkins University (advisor: Charles Meneveau), where his work on velocity gradient dynamics in turbulence won the Corrsin-Kovasznay award. He was then a postdoctoral fellow at the Center for Turbulence Research at Stanford University for three years, working on various topics related to small-scale turbulence, multiphase & particle-laden flows, and boundary layers. He joined the Mechanical and Aerospace Engineering department at the University of California, Irvine in 2020 as an assistant professor. His recent research on the energy cascade was featured in Physics Today, and his forthcoming review of multi-scale velocity gradient dynamics will appear in the next issue of Annual Review of Fluid Mechanics.

Wednesday, September 20, 2023

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/98121141178?pwd=VGEyaXVWYnRaazFYWUVhbVAycGVWQT09.

host: Spedding

Nonlinear Mechanics and Robotic Mechanisms using Coiled Muscles and Snapping Beams

Sameh Tawfick

Associate Professor
Department of Mechanical Science and Engineering
University of Illinois
Urbana, IL

My group is developing a roadmap for elastic actuating materials to replace bulky electric motors in miniature robots requiring large mechanical work output.

First, I will describe the mechanics of coiled muscles made by twisting nylon fishing lines, and how these actuators use internal strain energy to achieve a “record breaking” performance. Then I will describe intriguing hierarchical super-, and hyper-coiled artificial muscles which exploit the interplay between nonlinear mechanics and material microstructure. Next, I will describe their use to actuate the dynamic snapping of insect-scale jumping robots. The combination of strong but slow muscles with a fast-snapping beam gives rise to dynamic buckling cascade phenomena leading to effective robotic jumping mechanisms.

These examples shed light on the future of automation propelled by new bioinspired materials, nonlinear mechanics, and unusual manufacturing processes.

Sameh Tawfick is an Associate Professor of Mechanical Science and Engineering at the University of Illinois. He studies advanced materials, nonlinear mechanics, and manufacturing processes. Sam obtained his PhD from the University of Michigan, was a Postdoctoral Associate at the Massachusetts Institute of Technology, and a Beaufort Visiting Fellow in St. John’s College at the University of Cambridge in 2023 He is the recipient of young investigator awards from the US Air Force, ASME, SME, and Dean’s Award for Excellence in Research at Illinois. His teaching awards at the University of Illinois include The Everitt Award for Teaching Excellence, The Two-year Alumni Teaching Award, and The Engineering Council Stanley H. Pierce Award for Empathetic Student-faculty Cooperation.

Wednesday, September 27, 2023

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/98121141178?pwd=VGEyaXVWYnRaazFYWUVhbVAycGVWQT09.

host: Zhao

Teaching a Robot to Perform Surgery: From 3D Image Understanding to Deformable Manipulation

Michael Yip

Associate Professor
Electrical and Computer Engineering Department
UC San Diego
La Jolla, CA

Robot manipulation have made massive strides in the past few years, especially in grasping for warehouse logistics, due to the achievements in the computer vision and reinforcement learning communities. One area that has taken off much slower is in understanding how to manipulate deformable objects. For example, surgical robotics are used today via teleoperation from a human-in-the-loop, but replacing the human’s visual understanding and task performance with an AI remains a lofty and puzzling challenge. How do you build intuition and control of how to deform, stretch, or cut anatomical tissue, find hemorrhages and suction blood and bodily fluids from view, or simply localize your robot within a dynamically changing and deformable world in real-time?

In this talk, I will discuss our work that originates from trying to automate robotic surgery, but falls towards building new modeling and learning schemes for deformable robot manipulation and visual servoing. I will discuss how we analyze a multimodal spectrum of sensory information to solve real-to-sim and sim-to-real problems, while towing a fine line between physics-based models and the less-explainable yet highly successful latent space embeddings. I will show how these techniques apply not only to automating surgical robots but general robot manipulation in real-world scenes.

Michael Yip is an Associate Professor of Electrical and Computer Engineering at UC San Diego, IEEE RAS Distinguished Lecturer, Hellman Fellow, and Director of the Advanced Robotics and Controls Laboratory (ARCLab). His group currently focuses on solving problems in data-efficient and computationally efficient robot control and motion planning through the use of various forms of learning representations from imitation learning and reinforcement learning strategies. These techniques focus on solving problems with visually guided robot manipulation and locomotion on novel, dexterous platforms, including surgical robot manipulators, continuum robots, snake-like robots, and underwater systems. His work has been recognized through several best paper awards and nominations at ICRA and IROS, and RA-L, as well as recognitions including the NSF CAREER award and the NIH Trailblazer award. Dr. Yip was previously a Research Associate with Disney Research Los Angeles in 2014, a Visiting Professor at Stanford University in 2019, and a Visiting Professor with Amazon Robotics' Machine Learning and Computer Vision group in Seattle, WA in 2018. He received a B.Sc. in Mechatronics Engineering from the University of Waterloo, an M.S. in Electrical Engineering from the University of British Columbia, and a Ph.D. in Bioengineering from Stanford University.

Wednesday, October 11, 2023

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/98121141178?pwd=VGEyaXVWYnRaazFYWUVhbVAycGVWQT09.

host: Nguyen

Soft-Matter Engineering for Robotics and Wearables

Carmel Majidi

Clarence H. Adamson Professor of Mechanical Engineering
Department of Mechanical Engineering
Carnegie Mellon University
Pittsburgh, PA
http://sml.me.cmu.edu

Progress in soft lithography and soft materials integration have led to extraordinary new classes of soft-matter sensors, circuits, and transducers. These material technologies are composed almost entirely out of soft matter – elastomers, gels, and conductive fluids like liquid metal – and represent the building blocks for machines and electronics that are soft, flexible, and stretchable. Because of their intrinsic compliance and elasticity, such devices can be incorporated into soft, biologically-inspired robots or be worn on the body and operate continuously without impairing natural body motion. In this talk, I will review recent contributions from my research group in creating soft multifunctional materials for wearable electronics and soft robotics using these emerging practices in “soft-matter engineering.” In particular, I will focus on soft robots powered using shape memory materials and soft material architectures for highly stretchable digital electronics, wearable energy harvesting, and electrically-responsive actuation. When possible, I will show how the design and operation of these soft-matter technologies can be guided by theoretical modeling methods based on principles of mechanics and discrete differential geometry. In addition to presenting my own research in the field, I will also briefly review broader efforts and emerging challenges in utilizing soft multifunctional materials for applications in wearable electronics, bioelectronic interfaces, and soft robotics.

Carmel Majidi is the Clarence H. Adamson Professor of Mechanical Engineering at Carnegie Mellon University, where he leads the Soft Machines Lab. His lab is dedicated to the discovery of novel material architectures that allow machines and electronics to be soft, elastically deformable, and biomechanically compatible. Currently, his research is focused on modeling, design, and control of soft robotic systems as well as the developoment of multifunctional materials that exhibit unique combinations of mechanical, electrical, and thermal properties and can function as “artificial” skin, nervous tissue, and muscle. Carmel has received grants from industry and federal agencies along with early career awards from DARPA, ONR, AFOSR, and NASA to explore challenges in soft-matter engineering and robotics. Prior to arriving at CMU, Prof. Majidi had postdoctoral appointments at Harvard and Princeton Universities and received his PhD in Electrical Engineering at UC Berkeley.

Wednesday, October 18, 2023

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/98121141178?pwd=VGEyaXVWYnRaazFYWUVhbVAycGVWQT09.

host: Zhao

Cyber-Manufacturing: Delivering Manufacturing Services Over Web 3.0

Binil Starly

Motorola Professor of Manufacturing Systems
School of Manufacturing Systems & Networks
Arizona State University
Tempe, AZ

Cybermanufacturing enables the shared use of networked manufacturing infrastructure to deliver manufacturing resources on-demand while maximizing capacity utilization, reducing consumption of natural and material resources, and reducing costs to product design and manufacturing. This talk will highlight three areas where our group has contributed to the understanding of Cybermanufacturing systems – 1) With the explosive growth of 3D product models, the data contained in them may be used to democratize access and broaden those who are able to engage in product design and manufacturing; 2) Understanding of manufacturing capability available over the entire US through Natural Language Processing (NLP), and its interface with Large Language Models (like BERT & GPT-4); 3) Identification and Verification of Machines in the context of a Distributed Web of machines. In the future, the digital connection across factories will also lead to Manufacturing Networks that are highly agile, distributed, and resilient while considering the long-term consequences of sustainable industrial performance. Emerging digital technologies such as Pervasive Sensing, Computational Intelligence, Edge-Fog-Cloud Computing, Digital Twins, Smart Automation, Intelligent Collaborative Robots etc., open new possibilities in the design of smart collaborative physical and digital networks of factories.

Binil Starly serves as the Founding School Director and Professor in the School of Manufacturing Systems & Networks, one of 8 Schools within the Ira. A. Fulton Schools of Engineering at Arizona State University. He has over 20 years of experience in Digital manufacturing. His laboratory is working on technologies that merge the digital and the physical world towards advancing both discrete and continuous manufacturing. His work is supported by the US National Science Foundation, Department of Energy and the Department of Defense. He has received the NSF CAREER award, SME ‘20 Most Influential Professors in Smart Manufacturing’, SME Young Manufacturing Engineering Award (2011) and numerous teaching awards at the University of Oklahoma and North Carolina State University. His lab website is at: https://www.dimelab.org.

Wednesday, October 25, 2023

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/98121141178?pwd=VGEyaXVWYnRaazFYWUVhbVAycGVWQT09.

host: Chen

Interface Dislocations in Grain Boundaries and 2D Heterostructures using Smith Normal Bicrystallography

Nikhil Chandra Admal

Assistant Professor
Department of Mechanical Science and Engineering
University of Illinois at Urbana-Champaign
Urbana-Champaign, IL

Crystal interfaces exist in diverse materials systems in the form of grain and phase boundaries in threedimensional (3D) polycrystals and as heterointerfaces in two-dimensional (2D) heterostructures. Interfaces significantly influence the mechanical response of a material as observed in phenomena such as superplasticity and creep in polycrystals and the much sought-after electromechanical coupling in 2D materials that is responsible for correlated electron physics.

This talk is motivated by the central role interface plasticity plays in diverse crystalline systems. Recognizing interface dislocations as the primary carriers of interface plasticity, we will present a unified mathematical framework to construct interface dislocations in an arbitrary heterointerface. Our framework is driven by the Smith normal form (SNF) for integer matrices which enables us to systematically explore the bicrystallography of interfaces. Analogous to the definition of a bulk dislocation, which relies on the translational symmetry of a 3D lattice, the framework results in a translation symmetry of the interface that is integral to the definition of interface dislocations. Central to our framework are two lattices — coincident site lattice (CSL) and the displacement shift complete lattice (DSCL) — derived from the two lattices that constitute an interface. The CSL and the interface dislocations in 2D heterointerfaces are commonly referred to as the moir´e superlattice and strain solitons, respectively. In 3D GBs, bicrystallography informs that a step in the boundary possibly accompanies a interface dislocation, and together, they are referred to as a disconnection.

We will demonstrate the application of SNF bicrystallography to a) enumerate disconnection modes in arbitrary rational GBs, and quantify their energetics, and b) to study interface dislocations in bilayer graphene with a significant twist (> 10°). The constructive nature of the framework lends itself to an algorithmic implementation based exclusively on integer matrix algebra.

Nikhil Admal is an Assistant Professor in the Department of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign (UIUC). Before joining UIUC, Dr. Admal was a Postdoctoral Research Scholar at the University of California Los Angeles (UCLA). He received his Ph.D. in Aerospace Engineering and Mechanics from the University of Minnesota in 2014. His research interests focus on developing materials models at various length and times scales ranging from the atomic to the continuum scales, with emphasis on modeling interface phenomena. His research currently focusses on modeling recovery, recrystallization, and grain growth in crystalline materials to establish the process-structure relationship and on the mechanics of two-dimensional heterointerfaces. He received the NSF Faculty Early Career Development Program (CAREER) Award in 2023. Dr. Admal is a recipient of the Institute for Digital Research and Education (IDRE) Postdoctoral Fellowship at UCLA and the Doctoral Dissertation Fellowship from the University of Minnesota. Dr. Admal has published in various international journals, including The Journal of Chemical Physics, Journal of Mechanics and Physics of Solids, International Journal of Plasticity, Materials Theory, and the Journal of Elasticity.

Wednesday, November 1, 2023

3:30 PM

Seaver Science Library, Room 202 (SSL 202)

The Zoom webinar is at https://usc.zoom.us/j/98121141178?pwd=VGEyaXVWYnRaazFYWUVhbVAycGVWQT09.

host: Plucinsky

The Coastal Ocean Boundary Layer: Cross-Shore Structure, Bottom Roughness and Trapped Baroclinic Waves

Geno Pawlak

Professor
Department of Mechanical and Aerospace Engineering
University of California at San Diego
La Jolla, CA

In this talk I will describe analysis of the cross-shore structure of the coastal ocean boundary layer using velocity measurements from an autonomous underwater vehicle (AUV) along with time series observations of the alongshore pressure gradient. Ensemble phase averages of the alongshore pressure gradient and velocities from multiple AUV surveys reveal characteristics akin to the Stokes oscillating boundary layer, with the nearshore flow leading the offshore flow in phase and with a corresponding velocity attenuation at shallower depths. Analysis of the alongshore momentum balance allows estimation of the drag coefficient as a function of cross‐shore distance which compares favorably with roughness from LIDAR and AUV‐based mapping. Roughness data suggest that larger scales, with wavelengths comparable to the total depth, play a more significant role than smaller meter‐scale roughness in determining the drag on the tidal flow. I will also present observations that highlight the role of coastal trapped baroclinic waves in driving barotropic tidal flow on the inner shelf.

Gene Pawlak joined the UCSD Department of Mechanical and Aerospace Engineering in 2012. Before joining the Jacobs School of Engineering, he served as an associate professor in the Department of Ocean and Resources Engineering at the University of Hawaii at Manoa. Pawlak is a UC San Diego alumnus, having earned his Ph.D. from the Department of Applied Mechanics and Engineering Sciences (now Mechanical and Aerospace Engineering) in 1997.

Pawlak’s research is focused on coastal and estuarine turbulent mixing processes and their interactions with topographic features. He is particularly interested in the role of flow structure in mass and momentum transport as well as the generation of this structure by topography. Interactions occur via a variety of mechanisms including boundary layer separation and hydraulic flow response. His work presently focuses on dynamics of steady and oscillating flow over irregular boundaries as well as on the generation and evolution of large scale structure in stratified flow around coastal headlands. The influence of these boundary dynamics on sediment transport and on sediment—water column geochemical exchange processes is also of key interest. Other areas of interest include effects of offshore forcing on near-shore dynamics, cross-shore exchange processes, the interaction of flow with biological systems, stratified turbulence, autonomous vehicle applications and laboratory experimental methods.

Wednesday, November 8, 2023

3:30 PM

Seaver Science Library, Room 202 (SSL 102)

The Zoom webinar is at https://usc.zoom.us/j/98121141178?pwd=VGEyaXVWYnRaazFYWUVhbVAycGVWQT09.

host: Spedding

Unleashing the Power of AI for Precision Health: The Vital Role of Physiological and Nursing Data

Xiao Hu

Candler Chair Professor
Nell Hodgson Woodruff School of Nursing
Emory University
Atlanta, Georgia

Artificial intelligence (AI) has tremendous potential to advance clinical practice and patient care by providing clinicians augmented abilities to derive diagnostic and prognostic insights from various types of data. Medical images, structured data, clinical notes in electronic health record systems are data modalities that have so far received much attention. In addition to these data modalities in spotlight, continuous physiological data including electrocardiography, blood pressure, intracranial pressure, electroencephalography, photoplethysmography signals are part of standard of care, hence ubiquitously available for patients in acute care, and least susceptible to practice variations. Rich and dynamic pathophysiological information is embedded in these signals and yet there are no experts like radiologists dedicated to interpreting these signals at scale. Therefore, there is a vast amount of untapped information in these signals. In this keynote, we will explore three overarching approaches to process physiological data:

1. The single modality approach, where novel metrics are derived from a single signal, unveiling physiological insights that remain concealed in conventional patient monitors.
2. The multi-signal approach, which analyzes multiple signal modalities to elucidate the intrinsic interplay among different organ systems, providing more precise signatures of acute illnesses.
3. The multimodality approach, which integrates physiological data with other clinical information, enabling enhanced patient monitoring capabilities and more precise care delivery.

Bedside nurses play a pivotal role in continuously managing, interpreting, documenting, and communicating physiological data. However, they often face alarm fatigue due to inferior built-in algorithms of patient monitors. By harnessing the power of AI tools to process physiological data, we can alleviate this burden, elevate the nursing profession, and ultimately improve patient care outcomes.

Xiao Hu is Asa Griggs Candler Chair Professor at the Nell Hodgson Woodruff School of Nursing, associated faculty at the Departments of Computer Sciences and Biomedical Informatics, and PhD program faculty at the joint Biomedical Engineering program of Georgia Tech and Emory University. He also serves as the Associate Director of the Center for Data Science. In his remarkable career, he has held faculty positions at esteemed institutions like UCLA, UCSF, and Duke University. Dr. Hu's pioneering research lies at the intersection of computational and health sciences, using advanced algorithms to transform healthcare data into actionable patient care insights. His significant contributions include over 160 peer-reviewed publications, multiple NIH research projects, and nine US patents.

Wednesday, November 15, 2023, 2023

3:30 PM

Seaver Science Library, Room 202 (SSL 102)

The Zoom webinar is at https://usc.zoom.us/j/98121141178?pwd=VGEyaXVWYnRaazFYWUVhbVAycGVWQT09.

host: Pahlevan

Smart Additive Manufacturing

Chinedum (Chi) Okwudire

Professor of Mechanical Engineering and Miller Faculty Scholar
Department of Mechanical Engineering
University of Michigan
Ann Arbor, MI

There is a lot of excitement about the potential of smart manufacturing (involving the use of information, automation, computation, software, sensing, and networking technologies) to revolutionize the manufacturing industry, e.g., by boosting manufacturing quality and productivity at low cost. An excellent application for such “smart” technologies is additive manufacturing (AM), another area of manufacturing that is gaining a lot of traction but is plagued by quality, productivity and cost issues. In this talk, I will share some of my research results in smart AM, aimed at enhancing AM quality and productivity at low cost using smart technologies. Specifically, I will discuss our work on speeding up 3D printers at low cost using advanced controls and cloud computing. I will also discuss our new research on intelligent optimization of scan sequence to minimize thermal induced defects in laser powder bed fusion AM. Finally, I will give a brief overview of efforts I am leading at the University of Michigan to integrate smart AM into our educational curriculum.

Chinedum (Chi) Okwudire is a Professor of Mechanical Engineering and Miller Faculty Scholar at the University of Michigan. His research is focused on exploiting knowledge at the intersection of machine design, control and computing to boost the performance of manufacturing automation systems at low cost. Chi has received a number of awards including the NSF CAREER Award; SME Outstanding Young Manufacturing Engineer Award; and UC Berkeley’s Russell Severance Springer Visiting Professorship. He was recently selected by SME as one of the 25 leaders transforming manufacturing. He has co-authored a number of best-paper-award-winning papers in the areas of manufacturing automation, control and mechatronics.

Wednesday, November 29, 2023

3:30 PM

Seaver Science Library, Room 202 (SSL 102)

The Zoom webinar is at https://usc.zoom.us/j/98121141178?pwd=VGEyaXVWYnRaazFYWUVhbVAycGVWQT09.

host: HOSTLASTNAME

host: Chen