Physics-constrained neural networks for half-space seismic wave modeling

2023/10/10 by Yi S. Ding, Yi Ding, Su Chen +4
Earth and Planetary Sciences · Physics and Astronomy · #Model Reduction and Neural Networks #Seismic Imaging and Inversion Techniques #Seismic Waves and Analysis

paper · doi:10.1016/j.cageo.2023.105477

openalex publication_date 2023/10/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/27

Citations

• On the limited memory BFGS method for large scale optimization
• Simulations of Ground Motion in the Los Angeles Basin Based upon the Spectral-Element Method
• Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data
• Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations
• On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
• Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks
• When and why PINNs fail to train: A neural tangent kernel perspective
• Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions