Events

PhD Defense Towards Physics-Inspired Modeling of Multi-Agent Dynamics
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Speaker: Song Wen

Bio

Location : CBIM 22

Committee: 

Professor Dimitris Metaxas (Rutgers, chair)

Professor Konstantinos Michmizos

Professor Xintong Wang

Professor Xi Peng (external)

Event Type: PhD Defense

Abstract:  Multi-agent dynamics modeling aims to characterize the evolving states of multiple interacting agents in complex environments based on their past behaviors. It plays an important role in applications such as autonomous driving, crowd simulation and urban mobility analysis. This dissertation explores physics-inspired approaches to this problem, focusing on models which integrate physical knowledge with deep learning techniques. First, we propose a continuous-time latent variable model based on first-order neural ordinary differential equations to capture agent dynamics and interactions. To more explicitly encode physical laws, we extend this framework to second-order, acceleration-based modeling through dynamic interaction graphs. Finally, we introduce a diffusion-based generative model that captures multi-modal future behaviors conditioned on motion priors and agent interactions. Collectively, these contributions advance the modeling of multi-agent dynamics by unifying deterministic physics-based formulations, data-driven deep learning architectures, and generative models.

Organization: 

Contact  Professor Dimitris Metaxas (Chair)

Zoom Link: https://rutgers.zoom.us/j/92350708775?pwd=PNOTae850nTOqMHbaSrbArjTbUkZxa.1