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physics.geo-ph2026
Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks
Ryoichiro Agata, Tomohisa Okazaki
Physics-informed neural networks (PINNs) provide a mesh-free framework for solving PDE-constrained inverse problems, but their extension to Bayesian inversion still faces a fundame…
physics.geo-ph2025
Three-dimensional crustal deformation analysis using physics-informed deep learning
Tomohisa Okazaki, Takeo Ito, Kazuro Hirahara +3
Earthquake-related phenomena such as seismic waves and crustal deformation impact broad regions, requiring large-scale modeling with careful treatment of artificial outer boundarie…
physics.geo-ph2024
Scientific Machine Learning Seismology
Tomohisa Okazaki
Scientific machine learning (SciML) is an interdisciplinary research field that integrates machine learning, particularly deep learning, with physics theory to understand and predi…