Physics-Informed Deep Inverse Operator Networks for Solving PDE Inverse Problems
Fuente:
arXiv
Saved in:
| Main Authors: | Cho, Sung Woong, Son, Hwijae |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Collocation-based Robust Variational Physics-Informed Neural Networks (CRVPINN)
by: Łoś, Marcin, et al.
Published: (2024)
by: Łoś, Marcin, et al.
Published: (2024)
Python library supporting Discrete Variational Formulations and training solutions with Collocation-based Robust Variational Physics Informed Neural Networks (DVF-CRVPINN)
by: Służalec, Tomasz, et al.
Published: (2026)
by: Służalec, Tomasz, et al.
Published: (2026)
Augmenting MRI scan data with real-time predictions of glioblastoma brain tumor evolution using faster exponential time integrators
by: Pabisz, Magdalena, et al.
Published: (2024)
by: Pabisz, Magdalena, et al.
Published: (2024)
IGA-ODIL: Optimizing DIscretre robust Loss with Isogeometric Analysis to solve forward and inverse problems faster using machine learning tools
by: Paszyński, Maciej, et al.
Published: (2026)
by: Paszyński, Maciej, et al.
Published: (2026)
Learning a robust shape parameter for RBF approximation
by: Veiga, Maria Han, et al.
Published: (2024)
by: Veiga, Maria Han, et al.
Published: (2024)
GridapROMs.jl: Efficient reduced order modelling in the Julia programming language
by: Mueller, Nicholas, et al.
Published: (2025)
by: Mueller, Nicholas, et al.
Published: (2025)
Convolutional-neural-operator-based transfer learning for solving PDEs
by: Fan, Peng, et al.
Published: (2025)
by: Fan, Peng, et al.
Published: (2025)
HS-FNO: History-Space Fourier Neural Operator for Non-Markovian Partial Differential Equations
by: Shikhman, Lennon J.
Published: (2026)
by: Shikhman, Lennon J.
Published: (2026)
BiLO: Bilevel Local Operator Learning for PDE Inverse Problems
by: Zhang, Ray Zirui, et al.
Published: (2024)
by: Zhang, Ray Zirui, et al.
Published: (2024)
Stratified Sampling Algorithms for Machine Learning Methods in Solving Two-scale Partial Differential Equations
by: Avilés, Eddel Elí Ojeda, et al.
Published: (2024)
by: Avilés, Eddel Elí Ojeda, et al.
Published: (2024)
Analysis and improvement of a semi-Lagrangian exponential scheme for the shallow-water equations on the rotating sphere
by: Steinstraesser, João Guilherme Caldas, et al.
Published: (2024)
by: Steinstraesser, João Guilherme Caldas, et al.
Published: (2024)
Neural network Approximations for Reaction-Diffusion Equations -- Homogeneous Neumann Boundary Conditions and Long-time Integrations
by: Avilés, Eddel Elí Ojeda, et al.
Published: (2024)
by: Avilés, Eddel Elí Ojeda, et al.
Published: (2024)
High-order temporal parametric finite element methods for simulating solid-state dewetting
by: Gan, Xiaowen, et al.
Published: (2025)
by: Gan, Xiaowen, et al.
Published: (2025)
STM Image Analysis using Autoencoders
by: Binev, Peter, et al.
Published: (2025)
by: Binev, Peter, et al.
Published: (2025)
Bayesian BiLO: Bilevel Local Operator Learning for Efficient Uncertainty Quantification of Bayesian PDE Inverse Problems with Low-Rank Adaptation
by: Zhang, Ray Zirui, et al.
Published: (2025)
by: Zhang, Ray Zirui, et al.
Published: (2025)
Convex Physics Informed Neural Networks for the Monge-Ampère Optimal Transport Problem
by: Caboussat, Alexandre, et al.
Published: (2025)
by: Caboussat, Alexandre, et al.
Published: (2025)
Multi-Resolution Training-Enhanced Kolmogorov-Arnold Networks for Multi-Scale PDE Problems
by: Yang, Yu-Sen, et al.
Published: (2025)
by: Yang, Yu-Sen, et al.
Published: (2025)
CUQIpy: II. Computational uncertainty quantification for PDE-based inverse problems in Python
by: Alghamdi, Amal M A, et al.
Published: (2023)
by: Alghamdi, Amal M A, et al.
Published: (2023)
Learning Hamiltonian flows from numerical integrators and examples
by: Fang, Rui, et al.
Published: (2025)
by: Fang, Rui, et al.
Published: (2025)
A Structure-Preserving PML-Domain-Embedding Method for Acoustic Wave Scattering by Moving Objects
by: Gu, Xuelong, et al.
Published: (2026)
by: Gu, Xuelong, et al.
Published: (2026)
A semi-Lagrangian method for the direct numerical simulation of crystallization and precipitation at the pore scale
by: Perez, Sarah, et al.
Published: (2024)
by: Perez, Sarah, et al.
Published: (2024)
Error Analysis of the Deep Mixed Residual Method for High-order Elliptic Equations
by: Bai, Mengjia, et al.
Published: (2024)
by: Bai, Mengjia, et al.
Published: (2024)
Automated Synthesis of Quantum Algorithms via Classical Numerical Techniques
by: Huang, Yuxin, et al.
Published: (2024)
by: Huang, Yuxin, et al.
Published: (2024)
An adaptive Deep Ritz framework for second-order fully nonlinear partial differential equations
by: Caboussat, Alexandre, et al.
Published: (2026)
by: Caboussat, Alexandre, et al.
Published: (2026)
A Deep Uzawa-Lagrange Multiplier Approach for Boundary Conditions in PINNs and Deep Ritz Methods
by: Makridakis, Charalambos G., et al.
Published: (2024)
by: Makridakis, Charalambos G., et al.
Published: (2024)
Latent Twins
by: Chung, Matthias, et al.
Published: (2025)
by: Chung, Matthias, et al.
Published: (2025)
A Semismooth Newton Solver and its application to an $hp$-FE Discretization in Elastoplasticity
by: Bammer, Patrick, et al.
Published: (2025)
by: Bammer, Patrick, et al.
Published: (2025)
Error estimate based adaptive quadrature for layer potentials over axisymmetric surfaces
by: Krantz, David, et al.
Published: (2024)
by: Krantz, David, et al.
Published: (2024)
What is a POLYNOMIAL-TIME Computable L2-Function?
by: Bacho, Aras, et al.
Published: (2026)
by: Bacho, Aras, et al.
Published: (2026)
A Classical-Quantum Hybrid Architecture for Physics-Informed Neural Networks
by: Lantigua, Said, et al.
Published: (2025)
by: Lantigua, Said, et al.
Published: (2025)
Deep vs. Shallow: Benchmarking Physics-Informed Neural Architectures on the Biharmonic Equation
by: Srinivasan, Akshay Govind, et al.
Published: (2025)
by: Srinivasan, Akshay Govind, et al.
Published: (2025)
Semi-Lagrangian Finite-Element Exterior Calculus for Incompressible Flows
by: Tonnon, Wouter, et al.
Published: (2023)
by: Tonnon, Wouter, et al.
Published: (2023)
Small noise limits of Markov chains and the PageRank
by: Borkar, Vivek S, et al.
Published: (2025)
by: Borkar, Vivek S, et al.
Published: (2025)
Pricing and Hedging Strategies for Cross-Currency Equity Protection Swaps
by: Rutkowski, Marek, et al.
Published: (2024)
by: Rutkowski, Marek, et al.
Published: (2024)
Preconditioning for time-harmonic Maxwell's equations using the Laguerre transform
by: Terekhov, Andrew V.
Published: (2023)
by: Terekhov, Andrew V.
Published: (2023)
A data driven Koopman-Schur decomposition for computational analysis of nonlinear dynamics
by: Drmač, Zlatko, et al.
Published: (2023)
by: Drmač, Zlatko, et al.
Published: (2023)
Adaptive Multilevel Neural Networks for Parametric PDEs with Error Estimation
by: Schütte, Janina E., et al.
Published: (2024)
by: Schütte, Janina E., et al.
Published: (2024)
Conjugate Gradient Methods are Not Efficient: Experimental Study of the Locality Limitation
by: Rüde, Ulrich
Published: (2026)
by: Rüde, Ulrich
Published: (2026)
A Least-Squares-Based Neural Network (LS-Net) for Solving Linear Parametric PDEs
by: Baharlouei, Shima, et al.
Published: (2024)
by: Baharlouei, Shima, et al.
Published: (2024)
Uncertainty quantification in the Henry problem using the multilevel Monte Carlo method
by: Logashenko, Dmitry, et al.
Published: (2024)
by: Logashenko, Dmitry, et al.
Published: (2024)
Similar Items
-
Collocation-based Robust Variational Physics-Informed Neural Networks (CRVPINN)
by: Łoś, Marcin, et al.
Published: (2024) -
Python library supporting Discrete Variational Formulations and training solutions with Collocation-based Robust Variational Physics Informed Neural Networks (DVF-CRVPINN)
by: Służalec, Tomasz, et al.
Published: (2026) -
Augmenting MRI scan data with real-time predictions of glioblastoma brain tumor evolution using faster exponential time integrators
by: Pabisz, Magdalena, et al.
Published: (2024) -
IGA-ODIL: Optimizing DIscretre robust Loss with Isogeometric Analysis to solve forward and inverse problems faster using machine learning tools
by: Paszyński, Maciej, et al.
Published: (2026) -
Learning a robust shape parameter for RBF approximation
by: Veiga, Maria Han, et al.
Published: (2024)