Physics‐Informed Deep Learning for Forward and Inverse Modeling of Inplane Crustal Deformation

Abstract Methods for modeling crustal deformation related to earthquakes and plate motions have been developed to incorporate complex crustal structures and multi‐fidelity observations. A machine learning approach called physics‐informed neural networks (PINNs), which can solve both forward and inve...

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Bibliographic Details
Main Authors: Tomohisa Okazaki, Kazuro Hirahara, Takeo Ito, Masayuki Kano, Naonori Ueda
Format: Article
Language:English
Published: Wiley 2025-03-01
Series:Journal of Geophysical Research: Machine Learning and Computation
Online Access:https://doi.org/10.1029/2024JH000474
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