Cover Image

Functional-prior-based approaches to Bayesian PDE-constrained inversion using physics-informed neural networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Agata, Ryoichiro, Okazaki, Tomohisa
Format:	Preprint
Published:	2026
Subjects:	Geophysics Machine Learning Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Scientific Machine Learning Seismology
by: Okazaki, Tomohisa
Published: (2024)

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

Microseismic source imaging using physics-informed neural networks with hard constraints
by: Huang, Xinquan, et al.
Published: (2023)

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

HypoNet Nankai: Rapid hypocenter determination tool for the Nankai Trough subduction zone using physics-informed neural networks
by: Agata, Ryoichiro, et al.
Published: (2024)

DiffPINN: Generative diffusion-initialized physics-informed neural networks for accelerating seismic wavefield representation
by: Cheng, Shijun, et al.
Published: (2025)

Seismic inversion using hybrid quantum neural networks
by: Vashisth, Divakar, et al.
Published: (2025)

OkadaTorch: A Differentiable Programming of Okada Model to Calculate Displacements and Strains from Fault Parameters
by: Someya, Masayoshi, et al.
Published: (2025)

GaborPINN: Efficient physics informed neural networks using multiplicative filtered networks
by: Huang, Xinquan, et al.
Published: (2023)

Quantification of Uncertainty and Its Propagation in Seismic Velocity Structure and Earthquake Source Inversion
by: Agata, Ryoichiro
Published: (2024)

Bayesian geoacoustic inversion using mixture density network
by: Wu, Guoli, et al.
Published: (2020)

Physics-informed waveform inversion using pretrained wavefield neural operators
by: Huang, Xinquan, et al.
Published: (2025)

Stochastic full waveform inversion with deep generative prior for uncertainty quantification
by: Xie, Yuke, et al.
Published: (2024)

Dual-space posterior sampling for Bayesian inference in constrained inverse problems
by: Siahkoohi, Ali, et al.
Published: (2026)

Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness
by: Zhu, Min, et al.
Published: (2023)

Towards physics-informed neural networks for landslide prediction
by: Dahal, Ashok, et al.
Published: (2024)

Geological and Well prior assisted full waveform inversion using conditional diffusion models
by: Wang, Fu, et al.
Published: (2024)

On the role of memorization in learned priors for geophysical inverse problems
by: Siahkoohi, Ali, et al.
Published: (2026)

Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forecasting
by: Rice, Julian, et al.
Published: (2020)

Multi-frequency wavefield solutions for variable velocity models using meta-learning enhanced low-rank physics-informed neural network
by: Cheng, Shijun, et al.
Published: (2025)

Physics-informed deep learning quantifies propagated uncertainty in seismic structure and hypocenter determination
by: Agata, Ryoichiro, et al.
Published: (2024)

An effective physics-informed neural operator framework for predicting wavefields
by: Ma, Xiao, et al.
Published: (2025)

Diffusion priors enhanced velocity model building from time-lag images using a neural operator
by: Ma, Xiao, et al.
Published: (2025)

RED-DiffEq: Regularization by denoising diffusion models for solving inverse PDE problems with application to full waveform inversion
by: Shan, Siming, et al.
Published: (2025)

Prediction of Effective Elastic Moduli of Rocks using Graph Neural Networks
by: Chung, Jaehong, et al.
Published: (2023)

IntraSeismic: a coordinate-based learning approach to seismic inversion
by: Romero, Juan, et al.
Published: (2023)

Bayesian full waveform inversion with sequential surrogate model refinement
by: Meles, Giovanni Angelo, et al.
Published: (2025)

A novel neural network-based approach to derive a geomagnetic baseline for robust characterization of geomagnetic indices at mid-latitude
by: Kieokaew, Rungployphan, et al.
Published: (2024)

A finite element-based machine learning model for hydro-mechanical analysis of swelling behavior in clay-sulfate rocks
by: Taherdangkoo, Reza, et al.
Published: (2025)

A new practical and effective source-independent full-waveform inversion with a velocity-distribution supported deep image prior: Applications to two real datasets
by: Song, Chao, et al.
Published: (2025)

Toward Routing River Water in Land Surface Models with Recurrent Neural Networks
by: Lima, Mauricio, et al.
Published: (2024)

Enhancing Robustness Of Digital Shadow For CO2 Storage Monitoring With Augmented Rock Physics Modeling
by: Gahlot, Abhinav Prakash, et al.
Published: (2025)

Advancing Geological Carbon Storage Monitoring With 3d Digital Shadow Technology
by: Gahlot, Abhinav Prakash, et al.
Published: (2025)

Least-Squares-Embedded Optimization for Accelerated Convergence of PINNs in Acoustic Wavefield Simulations
by: Abedi, Mohammad Mahdi, et al.
Published: (2025)

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

Full waveform inversion with CNN-based velocity representation extension
by: Mu, Xinru, et al.
Published: (2025)

HydroStartML: A combined machine learning and physics-based approach to reduce hydrological model spin-up time
by: Pawusch, Louisa, et al.
Published: (2025)

Integrating Newton's Laws with deep learning for enhanced physics-informed compound flood modelling
by: Radfar, Soheil, et al.
Published: (2025)

Bayesian full waveform inversion with learned prior using deep convolutional autoencoder
by: Hu, Shuhua, et al.
Published: (2025)

Physics-Informed Calibration of Aeromagnetic Compensation in Magnetic Navigation Systems using Liquid Time-Constant Networks
by: Nerrise, Favour, et al.
Published: (2024)