26 citations · 30 across the 7 of their papers we have counts for
7 papers
ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning
Wenqian Chen, Zhi-Feng Wei, Yucheng Fu +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…
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…
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…
Physics-informed machine learning of the correlation functions in bulk fluids
Wenqian Chen, Peiyuan Gao, Panos Stinis
The Ornstein-Zernike (OZ) equation is the fundamental equation for pair correlation function computations in the modern integral equation theory for liquids. In this work, machine…
Physics-informed machine learning of redox flow battery based on a two-dimensional unit cell model
Wenqian Chen, Yucheng Fu, Panos Stinis
In this paper, we present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery, with its physics constraints enfor…
Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations
Wenqian Chen, Panos Stinis
Physics-informed neural networks have emerged as an alternative method for solving partial differential equations. However, for complex problems, the training of such networks can…