26 citations · 30 across the 7 of their papers we have counts for
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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★ 3 cited
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…
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…