12 papers
Large language models for partial differential equation workflows
Han Wan, Rui Zhang, Hao Sun
Partial differential equations (PDEs) become actionable in science and engineering not as isolated formulae, but as executable workflows that connect modelling assumptions, governi…
Physics Transformer: Tailoring Transformer for General PDE Prediction
Guoze Sun, Rui Zhang, Jiankai Tang +4
Transformer architectures have attracted increasing attention for solving partial differential equations (PDEs), owing to their flexibility in handling irregular discretizations an…
Optimization and Generation in Aerodynamics Inverse Design
Huaguan Chen, Ning Lin, Luxi Chen +5
Aerodynamic inverse design can improve vehicle and aircraft efficiency, but practical design rarely seeks performance alone: vehicle refinement must reduce drag while preserving vi…
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…
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…
UniFluids: Unified Neural Operator Learning with Conditional Flow-matching
Haosen Li, Qi Meng, Jiahao Li +4
Partial differential equation (PDE) simulation holds extensive significance in scientific research. Currently, the integration of deep neural networks to learn solution operators o…