Imagen de Portada

OkadaTorch: A Differentiable Programming of Okada Model to Calculate Displacements and Strains from Fault Parameters

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Someya, Masayoshi, Yamada, Taisuke, Okazaki, Tomohisa
Formato:	Preprint
Publicado:	2025
Materias:	Geophysics Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Physics-Informed Linear Model (PILM): Analytical Representations and Application to Crustal Strain Rate Estimation
por: Okazaki, Tomohisa
Publicado: (2025)

Scientific Machine Learning Seismology
por: Okazaki, Tomohisa
Publicado: (2024)

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks
por: Agata, Ryoichiro, et al.
Publicado: (2026)

Differentiable Inverse Modeling with Physics-Constrained Latent Diffusion for Heterogeneous Subsurface Parameter Fields
por: Lin, Zihan, et al.
Publicado: (2025)

An End-to-End Differentiable, Graph Neural Network-Embedded Pore Network Model for Permeability Prediction
por: Zhao, Qingqi, et al.
Publicado: (2025)

Fast ground penetrating radar dual-parameter full waveform inversion method accelerated by hybrid compilation of CUDA kernel function and PyTorch
por: Liu, Lei, et al.
Publicado: (2025)

Parameter-Efficient Transfer Learning for Microseismic Phase Picking Using a Neural Operator
por: Abdullin, Ayrat, et al.
Publicado: (2025)

Learning Generative Models for Lumped Rainfall-Runoff Modeling
por: Yang, Yang, et al.
Publicado: (2023)

EFKAN: A KAN-Integrated Neural Operator For Efficient Magnetotelluric Forward Modeling
por: Wang, Feng, et al.
Publicado: (2025)

Lithological Controls on the Permeability of Geologic Faults: Surrogate Modeling and Sensitivity Analysis
por: Lu, Hannah, et al.
Publicado: (2025)

Generative Latent Diffusion Model for Inverse Modeling and Uncertainty Analysis in Geological Carbon Sequestration
por: Feng, Zhao, et al.
Publicado: (2025)

GenAI4UQ: A Software for Inverse Uncertainty Quantification Using Conditional Generative Models
por: Fan, Ming, et al.
Publicado: (2024)

Real-Time Earthquake Magnitude Classification from Initial P-Waves: Models, Dataset, and Comparative Analysis for South Asia
por: Fahim, Md Nasiat Hasan, et al.
Publicado: (2026)

A Framework for Modeling Liquefaction-Induced Road Disruptions After Earthquakes: Implications for Emergency Response and Access in the Cascadia Region of North America
por: Sanger, Morgan D., et al.
Publicado: (2026)

Multimodal Atmospheric Super-Resolution With Deep Generative Models
por: Chakraborty, Dibyajyoti, et al.
Publicado: (2025)

SeisLM: a Foundation Model for Seismic Waveforms
por: Liu, Tianlin, et al.
Publicado: (2024)

Graph Network Surrogate Model for Subsurface Flow Optimization
por: Tang, Haoyu, et al.
Publicado: (2023)

Latent Diffusion Model for Conditional Reservoir Facies Generation
por: Lee, Daesoo, et al.
Publicado: (2023)

Initial Model Incorporation for Deep Learning FWI: Pretraining or Denormalization?
por: Chen, Ruihua, et al.
Publicado: (2025)

Diffusion Model-Based Posterior Sampling in Full Waveform Inversion
por: Taufik, Mohammad H., et al.
Publicado: (2025)

The Physics-Informed Neural Network Gravity Model: Generation III
por: Martin, John, et al.
Publicado: (2023)

Controlled Latent Diffusion Models for 3D Porous Media Reconstruction
por: Naiff, Danilo, et al.
Publicado: (2025)

Data-Driven Dynamic Friction Models based on Recurrent Neural Networks
por: Cortes, Gaëtan, et al.
Publicado: (2024)

Global River Forecasting with a Topology-Informed AI Foundation Model
por: Ren, Hancheng, et al.
Publicado: (2026)

Uncertainty-Driven Modeling of Microporosity and Permeability in Clastic Reservoirs Using Random Forest
por: Risha, Muhammad, et al.
Publicado: (2025)

Multi-Model Ensemble and Reservoir Computing for River Discharge Prediction in Ungauged Basins
por: Funato, Mizuki, et al.
Publicado: (2025)

Function-Space Decoupled Diffusion for Forward and Inverse Modeling in Carbon Capture and Storage
por: Ju, Xin, et al.
Publicado: (2026)

Seismic Data Interpolation via Denoising Diffusion Implicit Models with Coherence-corrected Resampling
por: Wei, Xiaoli, et al.
Publicado: (2023)

Fourier Neural Operator Surrogate Model to Predict 3D Seismic Waves Propagation
por: Lehmann, Fanny, et al.
Publicado: (2023)

Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting
por: Jafari, Alireza, et al.
Publicado: (2024)

Automated Classification of Volcanic Earthquakes Using Transformer Encoders: Insights into Data Quality and Model Interpretability
por: Suzuki, Y., et al.
Publicado: (2025)

SeisRDT: Latent Diffusion Model Based On Representation Learning For Seismic Data Interpolation And Reconstruction
por: Wang, Shuang, et al.
Publicado: (2025)

Extrapolability Improvement of Machine Learning-Based Evapotranspiration Models via Domain-Adversarial Neural Networks
por: Shi, Haiyang
Publicado: (2024)

Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression
por: Pradhan, Anshuman, et al.
Publicado: (2023)

Automatic Differentiation-based Full Waveform Inversion with Flexible Workflows
por: Liu, Feng, et al.
Publicado: (2024)

SeisFusion: Constrained Diffusion Model with Input Guidance for 3D Seismic Data Interpolation and Reconstruction
por: Wang, Shuang, et al.
Publicado: (2024)

Physical Sensitivity Kernels Can Emerge in Data-Driven Forward Models: Evidence From Surface-Wave Dispersion
por: Yu, Ziye, et al.
Publicado: (2026)

Thermal Earth Model for the Conterminous United States Using an Interpolative Physics-Informed Graph Neural Network (InterPIGNN)
por: Aljubran, Mohammad J., et al.
Publicado: (2024)

Three-dimensional crustal deformation analysis using physics-informed deep learning
por: Okazaki, Tomohisa, et al.
Publicado: (2025)

HydroAgent: Closing the Gap Between Frontier LLMs and Human Experts in Hydrologic Model Calibration via Simulator-Grounded RL
por: Li, Zhi, et al.
Publicado: (2026)