7 papers
From Embedding to Control: Representations for Stochastic Multi-Object Systems
Xiaoyuan Cheng, Yiming Yang, Wei Jiang +3
This paper studies how to achieve accurate modeling and effective control in stochastic nonlinear dynamics with multiple interacting objects. However, non-uniform interactions and…
Information Shapes Koopman Representation
Xiaoyuan Cheng, Wenxuan Yuan, Yiming Yang +4
The Koopman operator provides a powerful framework for modeling dynamical systems and has attracted growing interest from the machine learning community. However, its infinite-dime…
Safe and Stable Control via Lyapunov-Guided Diffusion Models
Xiaoyuan Cheng, Xiaohang Tang, Yiming Yang
Diffusion models have made significant strides in recent years, exhibiting strong generalization capabilities in planning and control tasks. However, most diffusion-based policies…
Fast-Forward Lattice Boltzmann: Learning Kinetic Behaviour with Physics-Informed Neural Operators
Xiao Xue, Marco F. P. ten Eikelder, Mingyang Gao +7
The lattice Boltzmann equation (LBE), rooted in kinetic theory, provides a powerful framework for capturing complex flow behaviour by describing the evolution of single-particle di…
Chaos Meets Attention: Transformers for Large-Scale Dynamical Prediction
Yi He, Yiming Yang, Xiaoyuan Cheng +4
Generating long-term trajectories of dissipative chaotic systems autoregressively is a highly challenging task. The inherent positive Lyapunov exponents amplify prediction errors o…
Learning Chaos In A Linear Way
Xiaoyuan Cheng, Yi He, Yiming Yang +5
Learning long-term behaviors in chaotic dynamical systems, such as turbulent flows and climate modelling, is challenging due to their inherent instability and unpredictability. The…