6 papers · 1 filter
Spatiotemporal System Forecasting with Irregular Time Steps via Masked Autoencoder
Kewei Zhu, Yanze Xin, Jinwei Hu +3
Predicting high-dimensional dynamical systems with irregular time steps presents significant challenges for current data-driven algorithms. These irregularities arise from missing…
How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?
Xiaoyuan Cheng, Wenxuan Yuan, Boyang Li +7
Diffusion policy sampling enables reinforcement learning (RL) to represent multimodal action distributions beyond suboptimal unimodal Gaussian policies. However, existing diffusion…
Outlier-robust Diffusion Posterior Sampling for Bayesian Inverse Problems
Yiming Yang, Xiaoyuan Cheng, Yi He +3
Diffusion models have emerged as powerful learned priors for Bayesian inverse problems (BIPs). Diffusion-based solvers rely on a presumed likelihood for the observations in BIPs to…
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…
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…
Tensor-Var: Efficient Four-Dimensional Variational Data Assimilation
Yiming Yang, Xiaoyuan Cheng, Daniel Giles +5
Variational data assimilation estimates the dynamical system states by minimizing a cost function that fits the numerical models with the observational data. Although four-dimensio…