Physics-Informed Machine Learning

  1. Physics-informed Bayesian neural networks (flow surrogate & reconstruction)
  • L. Sun*, J.-X. Wang, Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data, Theoretical and Applied Mechanics Letters, 10(3): 161-169, 2020 [Arxiv, DOI, bib]
  • L. Sun*, H. Gao*, S. Pan, J.-X. Wang. Surrogate Modeling for Fluid Flows Based on Physics-Constrained Deep Learning Without Simulation Data. Computer Methods in Applied Mechanics and Engineering, 361, 112732, 2020. [Arxiv, DOI, bib]
  • H. Gao*, J.-X. Wang, M. Zahr, Non-intrusive model reduction of large-scale, nonlinear dynamical systems using deep learning, Physica D: Nonlinear Phenomena, 412, 132614, 2020 [Arxiv, DOI, bib]

2. Physics-informed geometry-adaptive convolutional neural networks (surrogate, inverse modeling, super-resolution)

  1. H. Gao*, L. Sun, J.-X. Wang, PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain, 2020 [Arxiv, DOI, bib]
  2. H. Gao*, L. Sun, J.-X. Wang, Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels, 2020 (under review) [Arxiv, DOI, bib]
  3. L. Guo, S. Ye, J. Han, H. Zheng, H. Gao*, D. Chen, J.-X. Wang, C. Wang, SSR-VFD: Spatial Super-Resolution for Vector Field Data Analysis and Visualization, IEEE PacificVis 2020., 2020 [ArxivLink]