7 papers
Preconditioned Physics-Informed Neural Operator Training
Shizheng Wen, Siddhartha Mishra, Marius Zeinhofer
Neural operators are typically trained in a supervised fashion, which requires a dataset to be generated with a classical solver. Training them physics-informed, i.e., purely from…
Does Transolver really need a Transformer?
Shizheng Wen, Siddhartha Mishra
The widely used Transolver family of neural operators is based on physics-attention, which softly assigns the points of an unstructured mesh to a small number of slices, applies se…
MOSIV: Multi-Object System Identification from Videos
Chunjiang Liu, Xiaoyuan Wang, Qingran Lin +9
We introduce the challenging problem of multi-object system identification from videos, for which prior methods are ill-suited due to their focus on single-object scenes or discret…
Learning, Solving and Optimizing PDEs with TensorGalerkin: an efficient high-performance Galerkin assembly algorithm
Shizheng Wen, Mingyuan Chi, Tianwei Yu +5
We present a unified algorithmic framework for the numerical solution, constrained optimization, and physics-informed learning of PDEs with a variational structure. Our framework i…
torch-sla: Differentiable Sparse Linear Algebra with Adjoint Solvers and Sparse Tensor Parallelism for PyTorch
Mingyuan Chi, Shizheng Wen
Differentiable sparse linear algebra is foundational for scientific machine learning, yet PyTorch lacks a unified library for it: torch.sparse provides only low-level kernels and a…
Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains
Shizheng Wen, Arsh Kumbhat, Levi Lingsch +4
The very challenging task of learning solution operators of PDEs on arbitrary domains accurately and efficiently is of vital importance to engineering and industrial simulations. D…
RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains
Sepehr Mousavi, Shizheng Wen, Levi Lingsch +3
Learning the solution operators of PDEs on arbitrary domains is challenging due to the diversity of possible domain shapes, in addition to the often intricate underlying physics. W…