1 citations · 1 across the 11 of their papers we have counts for
11 papers
Large language models for partial differential equation workflows
Han Wan, Rui Zhang, Hao Sun
Numerical methods and deep learning have advanced the modelling and simulation of systems governed by partial differential equations (PDEs), while formulating equations, configurin…
PerFlow: Physics-Embedded Rectified Flow for Efficient Reconstruction and Uncertainty Quantification of Spatiotemporal Dynamics
Hao Zhou, Rui Zhang, Han Wan +1
Reconstructing PDE-governed fields from sparse and irregular measurements is challenging due to their ill-posed nature. Deterministic surrogates are trained on dense fields that st…
Geometry-Aware Neural Optimizer for Shape Optimization and Inversion
Guoze Sun, Tianya Miao, Haoyang Huang +4
Geometry is central to PDE-governed systems, motivating shape optimization and inversion. Classical pipelines conduct costly forward simulation with geometry processing, requiring…
Fast and Effective Computation of Generalized Symmetric Matrix Factorization
Lei Yang, Han Wan, Min Zhang +1
In this paper, we study a nonconvex, nonsmooth, and non-Lipschitz generalized symmetric matrix factorization model that unifies a broad class of matrix factorization formulations a…
Stable spectral neural operator for learning stiff PDE systems from limited data
Rui Zhang, Han Wan, Yang Liu +1
Accurate modeling of spatiotemporal dynamics is crucial to understanding complex phenomena across science and engineering. However, this task faces a fundamental challenge when the…
L2V-CoT: Cross-Modal Transfer of Chain-of-Thought Reasoning via Latent Intervention
Yuliang Zhan, Xinyu Tang, Han Wan +3
Recently, Chain-of-Thought (CoT) reasoning has significantly enhanced the capabilities of large language models (LLMs), but Vision-Language Models (VLMs) still struggle with multi-…
SlotPi: Physics-informed Object-centric Reasoning Models
Jian Li, Wan Han, Ning Lin +8
Understanding and reasoning about dynamics governed by physical laws through visual observation, akin to human capabilities in the real world, poses significant challenges. Current…
OmniFluids: Physics Pre-trained Modeling of Fluid Dynamics
Rui Zhang, Qi Meng, Han Wan +3
Computational fluid dynamics (CFD) drives progress in numerous scientific and engineering fields, yet high-fidelity simulations remain computationally prohibitive. While machine le…
PeSANet: Physics-encoded Spectral Attention Network for Simulating PDE-Governed Complex Systems
Han Wan, Rui Zhang, Qi Wang +2
Accurately modeling and forecasting complex systems governed by partial differential equations (PDEs) is crucial in various scientific and engineering domains. However, traditional…
Spectral-inspired Operator Learning with Limited Data and Unknown Physics
Han Wan, Rui Zhang, Hao Sun
Learning PDE dynamics from limited data with unknown physics is challenging. Existing neural PDE solvers either require large datasets or rely on known physics (e.g., PDE residuals…
PIMRL: Physics-Informed Multi-Scale Recurrent Learning for Burst-Sampled Spatiotemporal Dynamics
Han Wan, Qi Wang, Yuan Mi +2
Deep learning has shown strong potential in modeling complex spatiotemporal dynamics. However, most existing methods depend on densely and uniformly sampled data, which is often un…