AEM Colloquium Series

Minsen Yuan and Rushikesh Kabadi, June 26, 2:30pm

Trajectory Optimization for Energy-Sharing UAV-UGV with Multiple Task Locations

Energy-sharing UAV-UGV systems extend aerial endurance beyond battery limits by leveraging ground vehicles as mobile charging stations, enabling persistent autonomy in infrastructure-deprived environments. Existing UAV-UGV trajectory optimization models scale poorly due to discrete road network representations or integer decision variables, while the common assumption of full UAV recharge limits compatibility with wireless partial charging. To overcome these limitations, we propose a nonlinear program formulation for UAV-UGV trajectory optimization via the smoothing of disjunctive constraints. The resulting formulation is scalable and supports partial UAV recharge. We demonstrate the proposed NLP formulation on a one-UAV-one-UGV system with multiple task locations. Compared to mixed-integer nonlinear programs, the proposed formulation reduces computation time by orders of magnitude, demonstrating improved scalability while maintaining a comparable level of constraint satisfaction.

Minsen Yuan is a third-year Ph.D. student in the AEM department, advised by Prof. Yue Yu and co-advised by Prof. Ryan Caverly. His research focuses on trajectory optimization and optimal control for multi-agent autonomous systems. He enjoys swimming and snowboarding.

Behavior of Nanocrystalline Materials in Extreme Conditions

What happens to a material when it is struck at speeds several times the speed of sound? Can a metal that appears perfectly solid suddenly flow like a liquid, or fail in unexpected ways, when subjected to extreme forces? These questions are becoming increasingly important as engineers design materials for applications ranging from hypersonic vehicles and spacecraft to protective armor and advanced transportation systems. One promising class of materials is nanocrystalline materials, whose internal grain structure is thousands of times smaller than the width of a human hair. Shrinking structural features to the nanoscale can dramatically increase a material's strength, but it can also introduce new and sometimes surprising mechanisms of deformation and failure under extreme loading conditions. In this talk, we will explore how nanocrystalline materials respond to high speed impacts, where enormous pressures, temperatures, and deformation rates develop in millionths of a second. Through experiments and computer simulations, we will see how the behavior of materials changes and how we use these insights to design materials models and ultimately design materials that are stronger, tougher, and more resilient. Along the way, we will discuss how physics, materials science, and engineering come together to address challenges we face in high-speed impact and extreme environments.

Rushikesh Kabadi is a fourth year PhD student in the Multiscale Extreme Mechanics and Materials (MXMM) (called MAXIMUM) lab working with Prof. Ravindran. His work focuses on understanding behavior of materials in extreme conditions. His research combines experiments, computational modeling, and materials characterization to investigate how microstructure influences deformation, damage, and failure during high-strain-rate events such as ballistic impacts. His interest lies in shock physics of solids, dislocation dynamics, material modelling, and mechanics of materials. Outside research, he is a fitness enthusiast. He plays cricket professionally, climbs frequently, and does calisthenics.

Nihar and Carter Vu, June 12, 2:30pm

MFINDER: A Multi-Fidelity, Physics-Informed Framework for Recovering and Denoising Incomplete Full-Field Kinematic Measurements

Full-field Digital Image Correlation (DIC) measurements are central to data-driven constitutive modeling but are often corrupted by noise and missing data. We propose MFINDER (Multi-Fidelity Imputator and Denoiser), a physics-informed multi-fidelity framework for reconstructing smooth, complete displacement fields from noisy and incomplete DIC data of mechanical tests. MFINDER first calibrates a finite element (FE) model to available experimental measurements, producing a smooth low-fidelity field that reflects specimen geometry, loading, and boundary conditions. Gaussian Process Regression (GPR) then learns the discrepancy between sparse/noisy high-fidelity DIC data and the low-fidelity FE prediction, correcting FE-model bias while retaining physical smoothness and data efficiency. Additional constraints, including continuity, deformed specimen shape, and boundary conditions, further guide reconstruction. MFINDER is validated using synthetic DIC-like displacement fields from wedge indentation of a soft rectangular specimen and mode-I failure of a soft notched specimen. These cases include varying noise levels and spatially distributed missing data, especially near contact regions and crack/notch-induced strain concentrations. Compared with existing DIC imputation methods, MFINDER improves reconstruction accuracy by approximately an order of magnitude.

Nihar is a 2nd-year PhD student in the Soft Materials Mechanics lab, advised by Prof. Kshitiz Upadhyay. His motivation to pursue a Ph.D. stems from his curiosity about the complex biomechanics of soft tissues like the heart and the brain. His current research focuses on developing data-driven constitutive modeling techniques to understand and capture the response of brain tissue during traumatic brain injury (TBI). Besides his research in solid mechanics, he likes to sing and play the harmonium, and is trained in Indian Classical Music.

Numerical Simulation of Hypersonic Boundary Layers

Hypersonic vehicles experience extreme heat flux and drag, which can be dramatically intensified by turbulence and vortices transporting hot air from the shock towards the vehicle surface. Accurately quantifying the relevant flow field structures in the boundary layer and their contributions to the heating and drag is critical to understanding performance and enabling design studies of hypersonic vehicles. This talk will provide a high-level overview of computational fluid dynamics (CFD) simulations of several hypersonic ground tests and flight tests, including HiFire-1, HiFire-5, BOLT II, and Oberkampf. This talk will discuss the physics of laminar-turbulent boundary layer transition, as well as new tools being developed to reduce computational costs and improve accessibility for designers.

Carter Vu is a fourth year PhD student with Dr. Graham Candler. His work focuses on high-speed boundary layer instability and transition to turbulence, particularly on the BOLT II flight test. He has completed internships at Aerojet Rocketdyne and the Johns Hopkins Applied Physics Laboratory. In addition to his conference publications, Carter has given invited talks at Kennedy Space Center through the Astronaut Scholarship, as well as ONERA, the French Aerospace Laboratory, through a NATO collaboration.

Ping-Yen and JJ Serdoncillo, June 5, 2:30pm

Solar Sail Momentum Management Using Model Predictive Control

Solar sails leverage solar radiation pressure (SRP) for fuel-free space propulsion. However, environmental disturbance torques lead to reaction wheel angular momentum accumulation, necessitating robust momentum management. This research introduces a model predictive control (MPC) framework tailored for NASA’s Solar Cruiser mission, which utilizes an Active Mass Translator (AMT) and Reflectivity Control Devices (RCDs) as momentum management actuators under inherently coupled dynamics. Our work establishes the foundation of MPC-based momentum management for solar sails and bridges the gap between optimization theory and realistic mission requirements. Simulation results demonstrate that the proposed strategy successfully manages momentum growth in realistic scenarios, outperforming state-of-the-art methods.

Ping-Yen is a 3rd-year PhD student in the ARDC lab, advised by Professor Ryan Caverly. His research focuses on developing predictive control frameworks for solar sail momentum management. Beside aerospace systems control, he enjoys running and snowboarding as long as the weather plays nice.

Discrete Vortex Modeling of Flapping Propulsors

This study investigates thrust production and vortex shedding dynamics in purely heaving and purely pitching propulsors by simulating the flow with an inviscid 2D discrete vortex method (DVM). Simulations were performed over a range of Strouhal numbers (St=fA/U) to examine how unsteady force generation varies with kinematics, particularly between a flat plate executing a pure pitching vs a pure heaving motion. Although both motions generate similar reverse von Kármán vortex streets, their thrust-production mechanisms differ significantly. DMV sums forces from circulatory and non-circulatory mechanisms separately, and as such we can observe that when heaving, thrust associated with the generation and shedding of trailing-edge vortices, while pitching motion creates thrust via non-circulatory mechanisms such as added-mass effects caused by fluid acceleration during angular motion. A post processing finite-time Lyapunov exponent (FTLE) analysis reveals the coherent underlying structure of the flow that guides momentum transport and provides an explanation for these changes in propulsive mechanisms. In both cases, the additional fluid momentum caused by the plate motion is organized within and between shed vortices depending on the kinematic motion. The results demonstrate that visually similar wakes can arise from fundamentally different propulsion mechanisms, highlighting the benefit of combining a range of analyses (vortex dynamics, force decomposition, Lagrangian methods) to better understand bio-inspired propulsion.

JJ Serdoncillo is an international student from the Philippines and a fourth-year PhD student in the GreenFluids Lab. He earned his Bachelor's degree in Aerospace Engineering from Syracuse University before pursuing his Master's degree at University of Minnesota where he is currently continuing his PhD in Aerospace and Engineering Mechanics. He applies a range of experimental and computational analysis techniques to investigate wake dynamics and propulsive performance. He has also worked on immersive visualization of 3D experimental flow data using virtual reality, enabling more intuitive interaction with time-varying datasets and improved observation of physical flow features.

Abigail Hunter, April 24, 2:30pm

Mesoscale Investigation of Dislocation-Grain Boundary Interactions in Metals and Alloys

"Microstructure” refers to the large number of crystal grains and their corresponding boundaries that make up metals and alloys. A material’s ability to accommodate stress induced through mechanical loads is dependent on the ease with which dislocations can move through the microstructure to relieve accumulated stress. Grain boundaries (GBs) are the largest impediment to this motion – this is true to some extent regardless of the grain size. The GB structure defines whether a dislocation can transmit across a GB, be partially absorbed at the GB, or glide along the GB and re-emit, altering the GB structure. For poor alignment between grains, dislocations can pile-up against GBs, building localized internal stress regions that work harden the material. This preferential localization of strains and plastic deformation at specific microstructural sites are precursors to damage nucleation. Thus, understanding and predicting dislocation-grain boundary interactions are key for capturing mechanical response, but they are also incredibly complex in part due to the vast number of possible grain boundaries and corresponding dislocation interactions. This talk will focus on recent multiscale modeling efforts addressing dislocation-grain boundary interactions, with particular focus on methods/approaches that can be used to scale information.

Dr. Abigail Hunter earned a Ph.D. in Mechanical Engineering from Purdue University in 2011, and a B.S. degree in Mechanical Engineering from the University of Utah in 2006. Following her Ph.D., she joined Los Alamos National Laboratory (LANL) as a postdoctoral research associate in 2011 in the X Computational Physics (XCP) Division, and then converted to a staff scientist in 2012. She is currently the group leader for the Materials and Physical Data Group within XCP and the Deputy Director for the Institute of Materials Science at LANL. She is also the Editor-in-Chief for the ASME Journal of Engineering Materials and Technology, and an Associate Editor for the International Journal of Plasticity. In 2020, she received the Alum of the Year Award from the Department of Mechanical Engineering at the University of Utah in recognition for outstanding achievements in Mechanical Engineering and service to the community. In 2019 she was nationally recognized as a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) for work developing and implementing models addressing brittle damage and dislocation dynamics in metals, which are two capabilities designed to address questions concerning advanced manufacturing of new materials. Her research interests include modeling the strength and damage of metals and alloys at both the meso- and macro-scales, with specific interest in connections between microstructure, dislocation-based deformation behaviors, and overall material response.

Daniel Chung, April 10, 2:30pm

Fluid Mechanics of Riblets Drag Reduction

Riblets are a surface texture composed of tiny ribs applied on aircraft skin to reduce drag, which saves on fuel, increases the payload and extends the range. To the fast-moving turbulent air that flows over it, riblets turn out to be smoother, generating less skin friction, than a perfectly flat surface. However, riblet performance is highly sensitive to their cross-sectional shape and features, which is bad news because the micron-sized ribs, imperceptible to the naked eye and challenging to measure even with precision instruments, are impossible to manufacture and maintain perfectly. Thus, accurate tolerancing, not only for manufacture but also for lifetime wear planning and monitoring, is key to this technology, requiring predictive capability of the kind that derives from advances in basic understanding. In this regard, I will present some of the progress we have made in the last few years, building on decades of research, on the fluid mechanics of turbulence over riblet surfaces. The support of the Australian Research Council, Cooperative Research Australia and the U.S. Air Force Office of Scientific Research FA2386-23-1-4071 is gratefully acknowledged.

Daniel Chung is a professor in the Department of Mechanical Engineering at the University of Melbourne. He obtained his bachelor's degree in engineering and computer science from the University of Melbourne in 2003, and his PhD in aeronautics from Caltech in 2009. He was a postdoc at the Jet Propulsion Laboratory before joining the University of Melbourne in 2012. Daniel's research uses computational fluid dynamics, where he tries to distil turbulent flows into simplified problems and to build physics-based models for prediction. Recently, he has been interested in understanding and controlling turbulent flow and thermal convection over rough surfaces, riblets and moving wavy surfaces.

Teresa Portone, March 27, 2:30pm

Beyond Parametric Uncertainty: Methods for Model-form Uncertainty Quantification

Uncertainty quantification (UQ) is critical for informing decisions because it provides a measure of confidence in model predictions, given the uncertainties present in the model. While approaches to characterize uncertainties in model parameters are well established, it is less clear how to address uncertainties arising when the equations of a mathematical model are themselves uncertain—that is, when there is model-form uncertainty (MFU). MFU often arises in models of complex physical phenomena where (1) simplifications for computational tractability or (2) lack of knowledge leads to unknowns for which appropriate mathematical forms are undefined or may not exist. Left uncharacterized, MFU can lead to errors in the governing equations (model-form error) and inconsistencies between model outputs and experimental data (model discrepancy). In this talk, I introduce several approaches that have been developed to address MFU. I then present recent work developing a hybrid physics-data MFU representation of a reduced gas-surface chemistry model for hypersonic ablation. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525

Teresa Portone is Principal Member of the Technical Staff at Sandia National Laboratories in Albuquerque, NM. She holds a Ph.D. in Computational Science, Engineering, and Mathematics from the Oden Institute for Computational Engineering and Sciences at the University of Texas at Austin and a B.S. in Mathematics from the University of Alabama. She joined Sandia as a staff member in 2020. Her research focuses on developing and deploying methods to quantify uncertainty in computational models used for national security applications, with a particular focus on methods to quantify model-form uncertainty and model prediction reliability.

Kenshiro Oguri, February 27, 2:30pm

Stochastic Planning, Control, and Optimization for Spacecraft Autonomy

Safety assurance is critical for operating any autonomous vehicles. Yet, this principle is significantly challenged in space, where vehicles must operate in nonlinear environments with stringent constraints and large uncertainty. As a result, spacecraft autonomy requires constrained planning, control, and optimization under uncertainty. The demand for such capabilities will only increase as we expand the frontier of our exploration across and beyond the solar system. Motivated by these demands in space community, my research group at Purdue develops theory, algorithms, and software for provably safe planning, control, and optimization under uncertainty. In this talk, I will present how we can leverage stochastic control, uncertainty quantification, and optimization to address fundamental challenges in safety-assured spacecraft autonomy. I will also discuss the implication and broader impact of the theoretical results beyond space applications.

Dr. Kenshiro (Ken) Oguri is an Assistant Professor of Aeronautics and Astronautics at Purdue University. Ken's research interest includes orbital mechanics, control theory, stochastic systems, and optimization. At Purdue, he currently leads a research group of 14 graduate students. On the control-theoretic side, his research spans stochastic control, optimal control, nonlinear control, and optimization. On the space application front, his research addresses challenges in space exploration, navigation, and autonomy, in collaboration with NASA, JPL, AFOSR, Aerospace Corporation, and Draper Labs. He has published more than 110 journal/conference papers in these fields. His research has been recognized by NASA Early Career Faculty (ECF) award and multiple paper awards. Prior to joining Purdue faculty in 2022, he worked at NASA JPL and JAXA. He received his PhD from the University of Colorado Boulder in 2021, and MS and BS from the University of Tokyo in 2017 and 2015, respectively.

Peng Wei, February 20, 2:30pm

AI-powered Automated Air Traffic Control

Both the U.S. national airspace system and the low-altitude airspaces call for innovations in automated air traffic control. For the national airspace system, the FAA's Brand New Air Traffic Control System (BNATCS) needs effective automation tools for air traffic monitoring and conflict resolution to support human air traffic controllers; for the low-altitude airspaces hosting small unmanned aerial systems (sUASs) and the emerging electric vertical take-off and landing (eVTOL) aircraft, automated air traffic control is the only feasible way to accommodate the envisioned high-density, fast-temp flight operations. In this talk, the speaker will present the models and algorithms of using reinforcement learning (RL) and large language models (LLM) for automating air traffic control, specifically aircraft separation assurance and conflict resolution. In addition, he will also discuss the readiness and challenges of testing, certifying and implementing these artificial intelligence (AI) tools in safety-critical aviation applications.

Peng Wei is an associate professor in the Department of Mechanical and Aerospace Engineering at the George Washington University, with courtesy appointments at Electrical and Computer Engineering Department and Computer Science Department. By contributing to the intersection of control, optimization, machine learning, and artificial intelligence, he and his team develop autonomous functions and decision support tools for aviation, avionics and aerial robotic systems. His current focuses are (1) safety, efficiency, and scalability of aircraft autonomy, multi-agent autonomy and human-autonomy teaming; (2) aviation applications including air traffic management/control (ATM/C), airline operations, UAS traffic management (UTM), advanced air mobility (AAM), and aviation electrification; and (3) AI safety, security and certification for safety-critical systems. Prof. Wei is an AIAA Associate Fellow. He serves as an associate editor for AIAA Journal of Aerospace Information Systems (JAIS), AIAA Journal of Guidance, Control, and Dynamics (JGCD), and Journal of Open Aviation Science. He received his Ph.D. degree in Aerospace Engineering from Purdue University in 2013 and bachelor’s degree in Control Theory from Tsinghua University in 2007.

Dennis M. Kochmann, January 30, 2:30pm

Design of Architected Materials

Architected materials (also known as metamaterials) have become not only a popular solution for applications that require materials with superior, peculiar or extreme properties, but they also continue to present challenges for optimization and design across scales: given a combination of target properties, what structural realization offers exactly those target properties? This inverse challenge has been tackled by a variety of approaches – from classical topology optimization to machine learning-based generative design. Here, we will discuss several topical challenges in the inverse design of architected materials and present approaches for their design. This includes the multiscale optimization of elastic architected materials, the design of spatially graded metamaterials for wave guidance based on ray tracing, and the generative design of architected materials by methods of machine learning. For each example, we demonstrate how theory and simulations have enabled new design frameworks of architected materials for various applications, and how this field is still offering many challenges and opportunities.

Dennis M. Kochmann is Professor of Mechanics and Materials and Head of the Department of Mechanical and Process Engineering at ETH Zürich in Switzerland. He studied Mechanical Engineering at Ruhr-University Bochum (Germany) and Engineering Mechanics at the University of Wisconsin-Madison. After postdoc positions at Wisconsin and Caltech, he became Assistant Professor of Aerospace at the California Institute of Technology in 2011, and Professor of Aerospace in 2016, a position he held through 2019. In April 2017 he joined ETH Zürich as Professor of Mechanics and Materials. His research focuses on the link between microstructure and properties of natural and architected materials, which includes the development of theoretical, computational, and experimental methods to bridge across scales from nano to macro. His research has been recognized by, among others, IUTAM’s Bureau Prize in Solid Mechanics, GAMM’s Richard von Mises Prize, an NSF CAREER Award, ASME’s T.J.R. Hughes Young Investigator Award, an ERC Consolidator Grant, and IACM’s John Argyris Award. He serves as Associate Editor for ASME’s Applied Mechanics Reviews and Archive of Applied Mechanics, as Vice-Chair of the Swiss Community for Computational Methods in Applied Sciences (SWICCOMAS) and on the Board of Directors of the Society of Engineering Science (SES).

Bharath Ganapathisubramani, December 5, 2:30pm

Vortex Dominated Flows: Can’t live with them…Can’t live without them…

Vortex-dominated flows are in abundance in engineering applications and natural environment. Vortical structures influence not only the flow field but also have major implications on forces and moments experienced by objects as well as noise generated by them. In this talk, I will present results from work carried out in my group across different projects. We will focus on at least two case studies. The first is aimed at understanding the fluid-structure interactions in flow past porous bluff bodies while the second will focus on swimming efficiency of marine reptiles in Mesozoic era. These case studies will show that the behaviour of vortex interactions have a profound impact well beyond their specific application and that understanding these interactions can spawn new applications in varied areas including flow manipulation and bio-inspired vehicle design.

Bharath Ganapathisubramani is a Professor of Experimental Fluid Mechanics in the Department of Aeronautics and Astronautics at the University of Southampton. He completed his Masters and PhD in Aerospace Engineering at the University of Minnesota and an undergraduate degree in Naval Architecture and Ocean Engineering at the Indian Institute of Technology-Madras. He was an Assistant Professor at Imperial College London and moved to Southampton as an Associate Professor. He currently serves as an Associate Editor for Experiments in Fluids and Flow. He is a Fellow of Royal Aeronautical Society and the American Physical Society as well as an Associate Fellow of AIAA.

Jie Ding, November 21, 2:30pm

Designing Intelligent AI: Insights from Human Cognition

Modern AI systems are evolving from passive tools to autonomous agents capable of reasoning, learning, and collaboration. This talk explores emerging research directions in generative AI and foundational principles inspired by human cognition: continuous learning and adaptation, effective knowledge transfer, and multi-objective decision making. The discussion aims to stimulate thoughts on developing domain-specific AI that can operate reliably in complex, real-world environments.

Jie Ding (https://jding.org) is an Associate Professor at the School of Statistics, University of Minnesota. He received his Ph.D. in Engineering Sciences from Harvard University in 2017, joined UMN in 2018, and earned early tenure promotion in 2023. Jie's research lies at the intersection of AI, statistics, and scientific computing, with a current focus on AI scalability and trustworthiness. His work has been recognized with the NSF CAREER Award, ARO Young Investigator Award, Cisco Research Award, Meta Research Award, and several best paper honors. He created a new UMN course on Generative AI (STAT8105) with open-source course materials at https://genai-course.jding.org, which has attracted broad interest from both students and professionals.

Laura De Lorenzis, November 7, 2:30pm

Variational phase-field modeling of fracture: toward second-generation models

Variational phase-field modeling of fracture, first introduced in 2000 for brittle fracture of homogeneous and isotropic materials under predominant mode-I loading, has since evolved in multiple directions. Extensions now cover multiaxial stress states, heterogeneous and anisotropic materials, as well as ductile, dynamic, and rate-dependent fracture. The original model was derived from a variational reformulation of Griffith’s fracture criterion through regularization. However, in many subsequent extensions, the inherent rigidity of the variational framework has prompted the development of non-variational models, which trade the theoretical and practical advantages of the variational setting for greater flexibility in reproducing experimental observations. In this presentation, we explore strategies to enrich variational phase-field models with sufficient flexibility to overcome current limitations, potentially paving the way for a second generation of variational phase-field fracture models. Preliminary results will be shown on fracture under multiaxial stress states, fracture of anisotropic materials, and dynamic fracture.

Laura De Lorenzis received her Engineering degree and her PhD from the University of her hometown Lecce, in southern Italy, where she first stayed as Assistant and later as Associate Professor of Solid and structural mechanics. In 2013 she moved to the TU Braunschweig, Germany, as Professor and Director of the Institute of Applied Mechanics. There she was founding member and first Chair (2017-2020) of the Center for Mechanics, Uncertainty and Simulation in Engineering. Since 2020 she is Professor of Computational Mechanics at ETH Zürich, in the Department of Mechanical and Process Engineering. She was visiting scholar in several renowned institutions, including Chalmers University of Technology, the Hong Kong Polytechnic University, the Massachusetts Institute of Technology (as holder of a Fulbright Fellowship in 2006), the Leibniz University of Hannover (with an Alexander von Humboldt Fellowship in 2010-2011), the University of Texas at Austin and the University of Cape Town. She is the recipient of several prizes, including the RILEM L’Hermite Medal 2011, the AIMETA Junior Prize 2011, the IIFC Young Investigator Award 2012, the Euromech Solid Mechanics Fellowship 2022, the IACM Fellowship 2024, two best paper awards and two student teaching prizes. In 2011 she was awarded a European Research Council Starting Researcher Grant. She has delivered over 30 plenary lectures at international conferences and authored or co-authored more than 160 papers on international journals on different topics of computational and applied mechanics. Since 2023 she is Editor of Computer Methods in Applied Mechanics and Engineering.

Shaoshuai Mou, October 31, 2:30pm

Control and Learning for Autonomous Systems

Modern society has been relying more and more on engineering advance of autonomous systems, ranging from individual systems (such as a robotic arm for manufacturing, a self-driving car, or an autonomous vehicle for planetary exploration) to cooperative systems (such as a human-robot team, swarms of drones, etc). In this seminar we will discuss our recent research in integration of optimization, networks and learning to address fundamental challenges in enabling autonomous systems to be optimal, adaptive, cooperative and swarming. Especially we will discuss our most recent progress in developing a fundamental framework for learning and control in autonomous systems. The framework comes from a differentiation of Pontryagin’s Maximum Principle and is able to provide a unified solution to three classes of learning/control tasks, i.e. adaptive autonomy, inverse optimization, and system identification. We will also present applications of this framework into human-robot teaming, especially in enabling an autonomous system to take guidance from human operators, which is usually sparse and vague. In addition, we will briefly introduce our recent progress in autonomy in space.

Shaoshuai Mou is the Elmer Bruhn associate professor in the School of Aeronautics and Astronautics at Purdue University. He received a Ph.D. in Electrical Engineering at Yale University in 2014, and then worked as a postdoc researcher at MIT for a year. He joined Purdue University as a tenure-track assistant professor in 2015, and was promoted to be Associate Professor with Tenure in 2021. His research group Autonomous & Intelligent Multi-agent Systems (AIMS) lab has been focusing on advancing control theory with recent progress in optimization, networks and machine learning for autonomous and robotics systems, with particular research interest in inverse optimal control for learning-from-demonstrations in robotics, parameter adaptation in optimal control, integration of control with learning, human-robot teaming, and distributed algorithms for control and optimization in multi-agent systems. Mou co-directs Purdue’s Institute for Control, Optimization and Networks (ICON) , consisting of more than 100 faculty members from more than 15 departments across Purdue University, which aims to provide a research and education platform for control of autonomous and robotics systems.

Curt A. Bronkhorst, October 24, 2:30pm

Modeling the Statistical Thermomechanics of High-Stress Triaxiality Porosity-based Ductile Damage

Porosity-based ductile damage within polycrystalline metallic materials is known to be strongly dependent upon microstructural details for light to moderate shock loading conditions. This is presumed to be dictated by spatially distributed stress conditions and defect pore nucleation strength due to the statistical aggregate nature of the material. During shock loading, in addition to pore nucleation and growth, the material deforms via finite elastic and plastic mechanisms. The power delivered to the material during shock loading is distributed to each deformation mechanism as stored and dissipated power with change of temperature by both Thompson-Joule and plastic power dissipation effects. A new finite deformation probabilistic porosity-based ductile damage model for high triaxiality conditions is presented which represents pore nucleation by a new combined probability distribution for stress and pore nucleation strength distributions. This nucleation model is derived from experimental and computational physics data.

A new isotropic plasticity model is included in the damage model which accounts for both thermally activated and phonon-drag regimes of dislocation motion with dislocation density as the primary state variable. This model accounts for both stored energy via an effective temperature measure and thermal energy via kinetic- vibrational temperature. This formulation also proposes an expression for the Taylor-Quinney factor which is guided by second-law restrictions. Porosity growth is represented by a thick-walled sphere unit cell approach which allows for inertial resistance to growth and facilitated by plastic deformation. A governing equation for thick wall sphere growth due to applied external pressure is derived which also accounts for surface energy and kinetic energy. Closure of this governing equation is achieved with a reduced-order model of inertial power as a function of loading conditions.

This reduced-order model is derived from a thick-walled sphere computed database by employing the isotropic plasticity model to perform varying initial temperature and strain rate condition thick-walled sphere calculations. The finite deformation ductile damage model is thermodynamically consistent and accounts for energy partitioned to finite-elasticity, dislocation slip plasticity, dislocation energy storage, kinetic energy, surface energy, and thermal energy. The physics computation work will be presented and connections with experiments will be made. Results for the ductile damage model will also be presented and compared with plate-impact experiments conducted on high-purity tantalum.

Dr. Curt A. Bronkhorst is Harvey D. Spangler Professor of Applied Mechanics at the University of Wisconsin – Madison in the Department of Mechanical Engineering and associate appointments in the Nuclear Engineering Department and Materials Science and Engineering Department. Prior appointments include Senior Scientist in the Theoretical Division at Los Alamos National Laboratory and Senior Scientist at Weyerhaeuser Company. He is director of the Army Research Laboratory funded Center for Extreme Events in Structurally Evolving Materials and guest scientist at Los Alamos National Laboratory. Bronkhorst is emeritus Honorary Commander for the Wisconsin Air National Guard 115th Fighter Wing. He is fellow of the American Society of Mechanical Engineers and a vice-chair of the ASME Materials Division Executive Committee. He is Associate Editor of the International Journal of Plasticity and also president of Northland Partners, LLC.

Gianluca Iaccarino, October 17, 2:30pm

Data Science Tools for Studying Laser-Induced Ignition in a Rocket Combustor

Laser-induced ignition is envisioned as a lightweight and effective technology for second-stage boosters and low-orbit maneuvers. One of the critical design challenges is to ensure reliability while minimizing fuel waste and overpressure. The presence of variability in the propellant mixture at the time of the laser firing, the imprecision present in the laser targeting and other potential differences between the design scenarios and the real-world operations make the process highly stochastic. In a large project at Stanford, we have developed high-fidelity simulation tools to faithfully represent the high-speed turbulent propellant dynamics, the laser energy deposition and the combustion dynamics of reactive mixture. The computations, together with a companion experimental campaign form the basis of several data-science activities. We will summarize how we have used the datasets to: (1) build data-driven surrogates to study ignition reliability, (2) perform validation in latent spaces to compare 100s of experimental and computational realizations, (3) carry out uncertainty quantification and attribution studies using multi-fidelity ensembles, (4) develop machine learning tools to enhance experimental diagnostics.

Gianluca Iaccarino is the Robert Bosch Chair and Professor of the Mechanical Engineering Department at Stanford University. He received his PhD in Italy from the Politecnico di Bari (Italy) in 2005, and joined the faculty at Stanford in 2007. Since 2014, he has been the Director of the PSAAP Center at Stanford, funded by the US Department of Energy focused on multiphysics simulations, uncertainty quantification, data science and exascale computing. He received the US ACM Thomas Hughes Medal, the Presidential Early Career Award for Scientists and Engineers (PECASE) award and is a Fellow of APS and Senior Fellow of AIAA.

Paul Dye, October 3, 2:30pm

Shuttle, Houston – a Life in Aerospace Engineering

Paul Dye, former Lead Flight Director for the Space Shuttle program, will talk about his life in aerospace engineering, space flight operations, and experimental aviation. He will talk about how he reached the center seat of Mission Control after serving as a systems flight controller for his first decade at NASA, his missions docking with (and building) space stations, and how the lessons learned in thirty years of flying space shuttles can be applied in the world of aerospace engineering. He will also touch on his life building, maintaining, and flying experimental aircraft, and how his fifty years as a pilot served to enhance his ability leading air and space missions.

As the longest-serving NASA Flight Director in history, Paul Dye was in a leadership position for 38 Space Shuttle missions, nine of which he served as the Lead Flight Director, responsible for development and training for the mission, as well as real time execution of all facets of the shuttle flight. Coordinating the work of thousands of mission planners, flight controllers, trainers, and astronauts, Dye spent twenty years in the center seat of Mission Control. These years were preceded by twelve years spent as a systems flight controller, and more years spent as an International Space Station Flight Director before his retirement in 2013.

Paul Dye has over 50 years of aviation experience as an aerospace engineer, aircraft builder and pilot. His scope has ranged from restoring classic light aircraft to planning and leading manned spaceflights. His love of flying machines dates back to early childhood, and he became involved with full-sized aircraft as a teenager, rebuilding J-3 Cubs in Minnesota. He earned his degree in Aeronautical Engineering with a specialization in aircraft design and flight testing from the University of Minnesota in 1982. He has flown over 140 different types of aircraft, many of them experimental, and many of those on their first flights. Dye is a licensed Commercial pilot rated for single and multi-engine, instrument, seaplanes, gliders, and several experimental jet aircraft. He is also a licensed airframe and powerplant mechanic as well as an FAA Designated Airworthiness Representative for Experimental Aircraft. He has built five aircraft (an RV-3, RV-8, Dream Tundra, Subsonex, and an electric Xenos motor glider) and is working on his sixth (an F1 Rocket). He was awarded the SETP Spirit of Flight Award in 2025.

For 33 years, he worked in increasingly responsible roles within the US (NASA) Manned Space Program, both as a technical expert in spacecraft systems and, eventually, as the overall lead of many missions to space. The winner of many prestigious awards including the Johnson Space Center Director’s Commendation, the NASA Outstanding Leadership Medal, and four NASA Exceptional Service Medals, Dye is the author of “Shuttle, Houston: My Life in the Center Seat of Mission Control”, his 2020 book covering the shuttle years from the perspective on MCC. He is well-known as a risk-management specialist and advises designers and builders – as well as pilots – on ways to build and operate aircraft with greater margins of safety. He is Leadership Consultant and speaker available to corporations and groups who wish to better their organizations and people.

Paul Dye is a Lifetime Member of the EAA, a Fellow of the Explorers Club, and was inducted into the Minnesota Aviation Hall of Fame in 2024.

Professor Perry Leo, September 5, 2:30pm

Aerospace Engineering and Mechanics, University of Minnesota

The first meeting of the Fall 2025 Seminar Series (AEM 8000) will be a welcome speech from our department head, Professor Perry Leo.

Professor Leo studies phase transformation, pattern formation and material properties in complex, multiphase solids. Leo and his group use theoretical and numerical analysis to couple formation of microstructure in these materials to their properties, such as strength, fracture and fatigue resistance, and electrical and magnetic response. Leo’s work encompasses a range of materials, including composite materials, biological materials, metal alloys and liquid crystals.