4 papers
ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning
Wenqian Chen, Yucheng Fu, Zhi-Feng Wei +3
Learning solution operators on arbitrary geometries remains a central challenge in scientific machine learning, especially for many-query simulation, physics-informed learning, and…
Self-adaptive weighting and sampling for physics-informed neural networks
Wenqian Chen, Amanda Howard, Panos Stinis
Physics-informed deep learning has emerged as a promising framework for solving partial differential equations (PDEs). Nevertheless, training these models on complex problems remai…
Self-adaptive weights based on balanced residual decay rate for physics-informed neural networks and deep operator networks
Wenqian Chen, Amanda A. Howard, Panos Stinis
Physics-informed deep learning has emerged as a promising alternative for solving partial differential equations. However, for complex problems, training these networks can still b…
Efficient Transformer-Inspired Variants of Physics-Informed Deep Operator Networks
Zhi-Feng Wei, Wenqian Chen, Panos Stinis
Operator learning has emerged as a promising tool for accelerating the solution of partial differential equations (PDEs). The Deep Operator Networks (DeepONets) represent a pioneer…