Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation dataLuning Sun, Jianxun Wang, Han Gao et al.|Computer Methods in Applied Mechanics and Engineering|2019Cited by 972
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domainHan Gao, Jianxun Wang, Luning Sun|Journal of Computational Physics|2020Cited by 577
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approachHeng Xiao, Christopher J. Roy, Rui Sun et al.|Journal of Computational Physics|2016Cited by 304
Predictive large-eddy-simulation wall modeling via physics-informed neural networksXiang I. A. Yang, Heng Xiao, Jianxun Wang et al.|Physical Review Fluids|2019Cited by 289
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problemsHan Gao, Jianxun Wang, Matthew J. Zahr|Computer Methods in Applied Mechanics and Engineering|2022Cited by 281