AEM Events

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).

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.

Smart Decision-Making for Autonomous Systems in Space Exploration Missions

Many autonomous systems benefit from efficient operations and advanced autonomy levels in space exploration missions, such as human missions to Mars and on-orbit servicing, assembly, and manufacturing (OSAM) missions. Due to dynamic operating environments, complex system behaviors, and strict mission constraints, it is challenging to realize full autonomy with capabilities of fuel or energy-efficient operations. Without human intervention, real-time decision-making, including both motion planning and logic/reasoning decisions, plays a critical role in assuring the reliability and performance of such a system toward mission success. This talk will present our work on developing sophisticated modeling approaches, scalable optimization algorithms, and machine learning based optimal control methods that collectively contribute to advanced decision-making strategies for efficient autonomous systems in space exploration missions. The discussion will highlight applications in two distinct types of autonomous systems. This first concerns space vehicle real-time guidance for Mars entry, powered descent, and landing mission, where onboard propellant is limited and high precision landing is required. The second focuses on origami-inspired deployable systems for OSAM, where systems automatically adapt their shape/functionality to mission needs. The seminar will articulate our overarching goal: to achieve a high level of autonomy for these systems, enabling them to navigate dynamic environments, complex operational scenarios, and stringent mission constraints effectively.

Dr. Ran Dai is a professor in the School of Aeronautics and Astronautics at Purdue University. Before joining Purdue, she was the Netjets Assistant Professor at The Ohio State University. She received her B.S. degree in Automation Science from Beihang University and her M.S. and Ph.D. degrees in Aerospace Engineering from Auburn University. After graduation, she worked as an engineer in an automotive technology company, Dynamic Research, Inc., and then joined the University of Washington as a postdoctoral fellow. Dr. Dai’s research focuses on control of autonomous systems, numerical optimization, networked dynamical systems, and space robotics. She is an associate fellow of AIAA and a recipient of the NSF Career Award and NASA Early Faculty Career Award. Dr. Dai is serving as an associate editor of the Journal of Guidance, Navigation, and Control and IEEE Transactions on Aerospace and Electronic Systems.