12 citations · 12 across the 2 of their papers we have counts for
3 papers
math.NA2025
A Bi-fidelity based asymptotic-preserving neural network for the semiconductor Boltzmann equation and its inverse problem
Liu Liu, Xueyu Zhu, Zhenyi Zhu
This paper introduces a Bi-fidelity Asymptotic-Preserving Neural Network (BI-APNNs) framework, designed to efficiently solve forward and inverse problems for the semiconductor Bolt…
math-ph2024
Asymptotic-preserving neural networks for the semiconductor Boltzmann equation and its application on inverse problems
Liu Liu, Yating Wang, Xueyu Zhu +1
In this paper, we develop the Asymptotic-Preserving Neural Networks (APNNs) approach to study the forward and inverse problem for the semiconductor Boltzmann equation. The goal of…
math.NA2022★ 12 cited
Physics-informed neural networks for learning the homogenized coefficients of multiscale elliptic equations
Jun Sur Richard Park, Xueyu Zhu
Multiscale elliptic equations with scale separation are often approximated by the corresponding homogenized equations with slowly varying homogenized coefficients (the G-limit). Th…