7 papers
MENO: MeanFlow-Enhanced Neural Operators for Dynamical Systems
Tianyue Yang, Xiao Xue
Neural operators have emerged as powerful surrogates for dynamical systems due to their grid-invariant properties and computational efficiency. However, Fourier-based variants inhe…
Physical Fidelity Reconstruction via Improved Consistency-Distilled Flow Matching for Dynamical Systems
Sicheng Ma, Tianyue Yang, Xiuzhe Wu +1
Reconstructing high-fidelity flow fields from low-fidelity observations is a central problem in scientific machine learning, yet recent diffusion and flow-matching models typically…
Autoregressive One-Step Generative Modeling for Dynamical System Forecasting
Tianyue Yang, Xiao Xue
Fast surrogate modeling for high-dimensional physical dynamics requires more than low short-term error: useful models must roll out efficiently while preserving the statistical str…
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…
Machine learning for modelling unstructured grid data in computational physics: a review
Sibo Cheng, Marc Bocquet, Weiping Ding +20
Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for co…
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…