Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning. - Han Gao 005; Jian-Xun Wang 001; Matthew J. Zahr (CoRR, 2019)

SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization. - Li Guo; Shaojie Ye; Jun Han 010; Hao Zheng 006; Han Gao 005; Danny Z. Chen; Jian-Xun Wang 001; Chaoli Wang 001 (PacificVis, 2020)

A Bi-fidelity Ensemble Kalman Method for PDE-Constrained Inverse Problems. - Han Gao 005; Jian-Xun Wang 001 (CoRR, 2020)

Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems. - Han Gao 005; Matthew J. Zahr; Jian-Xun Wang 001 (CoRR, 2021)

PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain. - Han Gao 005; Luning Sun; Jian-Xun Wang 001 (J. Comput. Phys., 2021)

Predicting Physics in Mesh-reduced Space with Temporal Attention. - Xu Han 012; Han Gao 005; Tobias Pfaff; Jian-Xun Wang 001; Liping Liu 001 (ICLR, 2022)

PatchGT: Transformer Over Non-Trainable Clusters for Learning Graph Representations. - Han Gao 005; Xu Han 012; Jiaoyang Huang; Jian-Xun Wang 001; Liping Liu 001 (LoG, 2022)

Unifying Predictions of Deterministic and Stochastic Physics in Mesh-reduced Space with Sequential Flow Generative Model. - Luning Sun; Xu Han 012; Han Gao 005; Jian-Xun Wang 001; Liping Liu 001 (NeurIPS, 2023)

An adaptive model reduction method leveraging locally supported basis functions. - Han Gao 005; Matthew J. Zahr (CoRR, 2023)

Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation. - Han Gao 005; Xu Han 012; Xiantao Fan; Luning Sun; Li-Ping Liu 001; Lian Duan; Jian-Xun Wang 001 (CoRR, 2023)