4 papers
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-Informed Linear Model (PILM): Analytical Representations and Application to Crustal Strain Rate Estimation
Tomohisa Okazaki
Many physical systems are described by partial differential equations (PDEs), and solving these equations and estimating their coefficients or boundary conditions (BCs) from observ…
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…
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…