2022, Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems [Github Repository]
2021, Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control [Github Repository]
2021, Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels — parametric forward SR and boundary inference. [Github Repository]
2021, Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Steady-State Parametric PDEs on Irregular Domain [Github Repository]
2020, Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data [Github Repository]
2020, Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning [Github Repository]
