11 citations · 26 across the 16 of their papers we have counts for
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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…
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…
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…
Benchmarking neural surrogates on realistic spatiotemporal multiphysics flows
Runze Mao, Rui Zhang, Xuan Bai +21
Predicting multiphysics dynamics is computationally expensive and challenging due to the severe coupling of multi-scale, heterogeneous physical processes. While neural surrogates p…
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…
Deciphering and integrating invariants for neural operator learning with various physical mechanisms
Rui Zhang, Qi Meng, Zhi-Ming Ma
Neural operators have been explored as surrogate models for simulating physical systems to overcome the limitations of traditional partial differential equation (PDE) solvers. Howe…
Monte Carlo Neural PDE Solver for Learning PDEs via Probabilistic Representation
Rui Zhang, Qi Meng, Rongchan Zhu +5
In scenarios with limited available data, training the function-to-function neural PDE solver in an unsupervised manner is essential. However, the efficiency and accuracy of existi…
Rethinking Influence Functions of Neural Networks in the Over-parameterized Regime
Rui Zhang, Shihua Zhang
Understanding the black-box prediction for neural networks is challenging. To achieve this, early studies have designed influence function (IF) to measure the effect of removing a…