Physics-informed deep learning approach for modeling crustal deformation
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Physics-informed deep learning approach for modeling crustal deformation
Authors
Keywords
-
Journal
Nature Communications
Volume 13, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-11-19
DOI
10.1038/s41467-022-34922-1
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions
- (2022) Majid Rasht‐Behesht et al. JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
- Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
- (2022) Salvatore Cuomo et al. JOURNAL OF SCIENTIFIC COMPUTING
- A general approach to seismic inversion with automatic differentiation
- (2021) Weiqiang Zhu et al. COMPUTERS & GEOSCIENCES
- Paradox of Modeling Curved Faults Revisited with General Non-Hypersingular Stress Green’s Functions
- (2020) Dye SK Sato et al. GEOPHYSICAL JOURNAL INTERNATIONAL
- Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking
- (2020) S. Mostafa Mousavi et al. Nature Communications
- B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
- (2020) Liu Yang et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Impact of topography and three-dimensional heterogeneity on coseismic deformation
- (2019) Leah Langer et al. GEOPHYSICAL JOURNAL INTERNATIONAL
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data
- (2019) Yinhao Zhu et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
- (2019) Ameya D. Jagtap et al. JOURNAL OF COMPUTATIONAL PHYSICS
- P Wave Arrival Picking and First-Motion Polarity Determination With Deep Learning
- (2018) Zachary E. Ross et al. JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
- A unified deep artificial neural network approach to partial differential equations in complex geometries
- (2018) Jens Berg et al. NEUROCOMPUTING
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- (2018) M. Raissi et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Enabling large-scale viscoelastic calculations via neural network acceleration
- (2017) Phoebe M. R. DeVries et al. GEOPHYSICAL RESEARCH LETTERS
- An elastic/viscoelastic finite element analysis method for crustal deformation using a 3-D island-scale high-fidelity model
- (2016) Tsuyoshi Ichimura et al. GEOPHYSICAL JOURNAL INTERNATIONAL
- Effect of the Earth's surface topography on quasi-dynamic earthquake cycles
- (2015) Makiko Ohtani et al. GEOPHYSICAL JOURNAL INTERNATIONAL
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Human-level concept learning through probabilistic program induction
- (2015) B. M. Lake et al. SCIENCE
- Earthquake detection through computationally efficient similarity search
- (2015) C. E. Yoon et al. Science Advances
- Prevalence of viscoelastic relaxation after the 2011 Tohoku-oki earthquake
- (2014) Tianhaozhe Sun et al. NATURE
- A domain decomposition approach to implementing fault slip in finite-element models of quasi-static and dynamic crustal deformation
- (2013) B. T. Aagaard et al. JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
- A unified continuum representation of post-seismic relaxation mechanisms: semi-analytic models of afterslip, poroelastic rebound and viscoelastic flow
- (2010) Sylvain Barbot et al. GEOPHYSICAL JOURNAL INTERNATIONAL
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search