SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain
Pu Ren(Northeastern University), Yang Liu(Northeastern University), Chengping Rao(Northeastern University), Jianxun Wang(Kunming University of Science and Technology), Hao Sun(University of Surrey), Su Chen(Beijing University of Technology)
Cited by 84
Related Papers
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
|Computer Methods in Applied Mechanics and Engineering|2019|972
PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain
|Journal of Computational Physics|2020|577
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach
|Journal of Computational Physics|2016|304
Predictive large-eddy-simulation wall modeling via physics-informed neural networks
|Physical Review Fluids|2019|289
Physics-informed graph neural Galerkin networks: A unified framework for solving PDE-governed forward and inverse problems
|Computer Methods in Applied Mechanics and Engineering|2022|281