Numerical Solution of Inverse Problems by Weak Adversarial Networks. - Gang Bao 001; Xiaojing Ye; Yaohua Zang; Haomin Zhou 001 (CoRR, 2020)

Weak adversarial networks for high-dimensional partial differential equations. - Yaohua Zang; Gang Bao 001; Xiaojing Ye; Haomin Zhou 001 (J. Comput. Phys., 2020)

A Machine Learning Enhanced Algorithm for the Optimal Landing Problem. - Yaohua Zang; Jihao Long; Xuanxi Zhang; Wei Hu; Weinan E; Jiequn Han (MSML, 2022)

Uncertainty-Driven Spiral Trajectory for Robotic Peg-in-Hole Assembly. - Hanwen Kang; Yaohua Zang; Xing Wang 016; Yaohui Chen 002 (IEEE Robotics Autom. Lett., 2022)

ParticleWNN: a Novel Neural Networks Framework for Solving Partial Differential Equations. - Yaohua Zang; Gang Bao 001 (CoRR, 2023)

Weak neural variational inference for solving Bayesian inverse problems without forward models: applications in elastography. - Vincent C. Scholz; Yaohua Zang; Phaedon-Stelios Koutsourelakis (CoRR, 2024)

Solving optimal control problems of rigid-body dynamics with collisions using the hybrid minimum principle. - Wei Hu; Jihao Long; Yaohua Zang; Weinan E; Jiequn Han (Commun. Nonlinear Sci. Numer. Simul., 2025)

DGenNO: a novel physics-aware neural operator for solving forward and inverse PDE problems based on deep, generative probabilistic modeling. - Yaohua Zang; Phaedon-Stelios Koutsourelakis (J. Comput. Phys., 2025)

DGNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling. - Yaohua Zang; Phaedon-Stelios Koutsourelakis (CoRR, 2025)