Bogdan Epureanu
Arthur F. Thurnau Professor of Mechanical Engineering
Bogdan Epureanu is an Arthur F. Thurnau Professor in the Department of Mechanical Engineering at the University of Michigan and has a courtesy appointment as Professor of Electrical Engineering and Computer Science. His research focuses on nonlinear dynamics of complex systems, such as teaming of autonomous vehicles, enhanced aircraft safety and performance, early detection of neurodegenerative diseases, forecasting tipping points in disease epidemics and ecology. His research brings together interdisciplinary teams and consortia such as Government (DOD, NIH, NSF, DOE), Industry (Ford, Pratt & Whitney, GE, Airbus), and Academia. He has published over 350 articles in journals, conferences, and books. He is Editor-in-Chief of ASME Journal for Computational and Nonlinear Dynamics, and has served on several editorial boards. He is the Director of the Automotive Research Center (ARC).
Projects
- Advanced Models for Fatigue Life Predictions of Hybrid Electric Vehicle Batteries
- Strategic Adaptive Vehicle Systems Feasibility Study
- Novel Hybrid Electric Powertrains Enabled by Models of Electro-Magnetic-Structural Dynamics
- AI-Based Attacker-Defender Dynamics of Adaptable Fleets of Autonomous Vehicles
- Structural Dynamic Modeling and Analysis of Damaged Vehicles
- Optimal Distribution of Tasks in Human-Autonomy Teams
- Dynamic Teaming of Autonomous Vehicles to Address Intelligent Adversarial Actions
- ADAS Tools for Verification, Validation and Development in Synthetic Environments
- Perception in Complex Scenes using Automatically Labeled Sonar-imaging Data in Synthetic Environments
- Adversarial Scene Generation for Virtual Validation and Testing of Off-Road Autonomous Vehicle Performance
- Resilient Trajectory Planning for Extreme Mobility on Challenging Slopes
- Automated Co-Design of Vehicles and their Teaming Operations for Optimal Off-Road Performance
- LLM-Enabled Operation Management of Multi-Agent Systems
- Trajectory Planning with Omni-Experiential Learning for Robust Fleet Mobility on Extreme Off-Road Terrain