3 citations · 6 across the 8 of their papers we have counts for
9 papers
Brep2Shape: Boundary and Shape Representation Alignment via Self-Supervised Transformers
Yuanxu Sun, Yuezhou Ma, Haixu Wu +4
Boundary representation (B-rep) is the industry standard for computer-aided design (CAD). While deep learning shows promise in processing B-rep models, existing methods suffer from…
Transolver-3: Scaling Up Transformer Solvers to Industrial-Scale Geometries
Hang Zhou, Haixu Wu, Haonan Shangguan +4
Deep learning has emerged as a transformative tool for the neural surrogate modeling of partial differential equations (PDEs), known as neural PDE solvers. However, scaling these s…
Exploring Accuracy Law for Deep Time Series Forecasters: An Empirical Study
Yuxuan Wang, Haixu Wu, Yuezhou Ma +8
Deep time series forecasting has emerged as a rapidly growing field in recent years. Despite the exponential growth of community interests, progress on standard benchmarks is often…
PhySense: Sensor Placement Optimization for Accurate Physics Sensing
Yuezhou Ma, Haixu Wu, Hang Zhou +3
Physics sensing plays a central role in many scientific and engineering domains, which inherently involves two coupled tasks: reconstructing dense physical fields from sparse obser…
FlashBias: Fast Computation of Attention with Bias
Haixu Wu, Minghao Guo, Yuezhou Ma +4
Attention with bias, which extends standard attention by introducing prior knowledge as an additive bias matrix to the query-key scores, has been widely deployed in vision, languag…
ProPINN: Demystifying Propagation Failures in Physics-Informed Neural Networks
Yuezhou Ma, Haixu Wu, Hang Zhou +3
Physics-informed neural networks (PINNs) have earned high expectations in solving partial differential equations (PDEs), but their optimization usually faces thorny challenges due…
Unisolver: PDE-Conditional Transformers Towards Universal Neural PDE Solvers
Hang Zhou, Yuezhou Ma, Haixu Wu +2
Deep models have recently emerged as promising tools to solve partial differential equations (PDEs), known as neural PDE solvers. While neural solvers trained from either simulatio…
RoPINN: Region Optimized Physics-Informed Neural Networks
Haixu Wu, Huakun Luo, Yuezhou Ma +2
Physics-informed neural networks (PINNs) have been widely applied to solve partial differential equations (PDEs) by enforcing outputs and gradients of deep models to satisfy target…
HelmFluid: Learning Helmholtz Dynamics for Interpretable Fluid Prediction
Lanxiang Xing, Haixu Wu, Yuezhou Ma +2
Fluid prediction is a long-standing challenge due to the intrinsic high-dimensional non-linear dynamics. Previous methods usually utilize the non-linear modeling capability of deep…