1 citations · 1 across the 2 of their papers we have counts for
2 papers
physics.comp-ph2023★ 1 cited
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…
cs.LG2023
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…