447 citations · 521 across the 6 of their papers we have counts for
6 papers
Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospects
Zhentao Tan, Yuze Hao, Boyi Zou +3
Solving inverse partial differential equation (PDE) problems is a fundamental topic in scientific research due to its broad significance across a wide range of real-world applicati…
A unified physics-informed generative operator framework for general inverse problems
Gang Bao, Yaohua Zang
Solving inverse problems governed by partial differential equations (PDEs) is central to science and engineering, yet remains challenging when measurements are sparse, noisy, or wh…
ParticleWNN: a Novel Neural Networks Framework for Solving Partial Differential Equations
Yaohua Zang, Gang Bao
Deep neural networks (DNNs) have been widely used to solve partial differential equations (PDEs) in recent years. In this work, a novel deep learning-based framework named Particle…
Numerical Solution of Inverse Problems by Weak Adversarial Networks
Gang Bao, Xiaojing Ye, Yaohua Zang +1
We consider a weak adversarial network approach to numerically solve a class of inverse problems, including electrical impedance tomography and dynamic electrical impedance tomogra…
Weak Adversarial Networks for High-dimensional Partial Differential Equations
Yaohua Zang, Gang Bao, Xiaojing Ye +1
Solving general high-dimensional partial differential equations (PDE) is a long-standing challenge in numerical mathematics. In this paper, we propose a novel approach to solve hig…
A Jump Stochastic Differential Equation Approach for Influence Prediction on Information Propagation Networks
Yaohua Zang, Gang Bao, Xiaojing Ye +2
We propose a novel problem formulation of continuous-time information propagation on heterogenous networks based on jump stochastic differential equations (SDE). The structure of t…