Deep learning methods for inverse problems using connections between proximal operators and Hamilton-Jacobi equations
Fuente:
arXiv
Saved in:
| Main Authors: | Akande, Oluwatosin, Langlois, Gabriel P., Onwunta, Akwum |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Momentum-based minimization of the Ginzburg-Landau functional on Euclidean spaces and graphs
by: Akande, Oluwatosin, et al.
Published: (2024)
by: Akande, Oluwatosin, et al.
Published: (2024)
Deep Nonnegative Matrix Factorization with Beta Divergences
by: Leplat, Valentin, et al.
Published: (2023)
by: Leplat, Valentin, et al.
Published: (2023)
Fast-forwarding quantum algorithms for linear dissipative differential equations
by: An, Dong, et al.
Published: (2024)
by: An, Dong, et al.
Published: (2024)
Smoothed Moreau-Yosida Tensor Train Approximation of State-constrained Optimization Problems under Uncertainty
by: Antil, Harbir, et al.
Published: (2023)
by: Antil, Harbir, et al.
Published: (2023)
Neural Hamilton--Jacobi Characteristic Flows for Optimal Transport
by: Park, Yesom, et al.
Published: (2025)
by: Park, Yesom, et al.
Published: (2025)
Adaptive operator learning for infinite-dimensional Bayesian inverse problems
by: Gao, Zhiwei, et al.
Published: (2023)
by: Gao, Zhiwei, et al.
Published: (2023)
Convex-concave splitting for the Allen-Cahn equation leads to $\varepsilon^2$-slow movement of interfaces
by: Dondl, Patrick, et al.
Published: (2025)
by: Dondl, Patrick, et al.
Published: (2025)
DeepONet-accelerated Bayesian inversion for moving boundary problems
by: Iglesias, Marco A., et al.
Published: (2025)
by: Iglesias, Marco A., et al.
Published: (2025)
Deep set based operator learning with uncertainty quantification
by: Ma, Lei, et al.
Published: (2025)
by: Ma, Lei, et al.
Published: (2025)
Optimal deep learning of holomorphic operators between Banach spaces
by: Adcock, Ben, et al.
Published: (2024)
by: Adcock, Ben, et al.
Published: (2024)
Learning truly monotone operators with applications to nonlinear inverse problems
by: Belkouchi, Younes, et al.
Published: (2024)
by: Belkouchi, Younes, et al.
Published: (2024)
Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
by: Xu, Tengfei, et al.
Published: (2023)
by: Xu, Tengfei, et al.
Published: (2023)
Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
by: Zhang, Benjamin J., et al.
Published: (2025)
by: Zhang, Benjamin J., et al.
Published: (2025)
Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
by: Cao, Lianghao, et al.
Published: (2024)
by: Cao, Lianghao, et al.
Published: (2024)
Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs
by: Hong, Youngjoon, et al.
Published: (2024)
by: Hong, Youngjoon, et al.
Published: (2024)
Nonlinear model reduction for operator learning
by: Eivazi, Hamidreza, et al.
Published: (2024)
by: Eivazi, Hamidreza, et al.
Published: (2024)
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations
by: Jentzen, Arnulf, et al.
Published: (2023)
by: Jentzen, Arnulf, et al.
Published: (2023)
Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
by: Ivagnes, Anna, et al.
Published: (2022)
by: Ivagnes, Anna, et al.
Published: (2022)
Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
by: Wang, Yizheng, et al.
Published: (2024)
by: Wang, Yizheng, et al.
Published: (2024)
Physics-aware deep learning framework for the limited aperture inverse obstacle scattering problem
by: Yin, Yunwen, et al.
Published: (2024)
by: Yin, Yunwen, et al.
Published: (2024)
Meshless solutions of PDE inverse problems on irregular geometries
by: Roggeveen, James V., et al.
Published: (2025)
by: Roggeveen, James V., et al.
Published: (2025)
Deep learning based numerical approximation algorithms for stochastic partial differential equations
by: Beck, Christian, et al.
Published: (2020)
by: Beck, Christian, et al.
Published: (2020)
Tensor train solution to uncertain optimization problems with shared sparsity penalty
by: Antil, Harbir, et al.
Published: (2024)
by: Antil, Harbir, et al.
Published: (2024)
Learning cardiac activation and repolarization times with operator learning
by: Centofanti, Edoardo, et al.
Published: (2025)
by: Centofanti, Edoardo, et al.
Published: (2025)
Variationally correct operator learning: Reduced basis neural operator with a posteriori error estimation
by: Qiu, Yuan, et al.
Published: (2025)
by: Qiu, Yuan, et al.
Published: (2025)
The learned range test method for the inverse inclusion problem
by: Sun, Shiwei, et al.
Published: (2024)
by: Sun, Shiwei, et al.
Published: (2024)
Physics-informed neural networks for operator equations with stochastic data
by: Escapil-Inchauspé, Paul, et al.
Published: (2022)
by: Escapil-Inchauspé, Paul, et al.
Published: (2022)
Dilated convolution neural operator for multiscale partial differential equations
by: Xu, Bo, et al.
Published: (2024)
by: Xu, Bo, et al.
Published: (2024)
Harnessing physics-informed operators for high-dimensional reliability analysis problems
by: Navaneeth, N, et al.
Published: (2024)
by: Navaneeth, N, et al.
Published: (2024)
Meshless method stencil evaluation with machine learning
by: Rot, Miha, et al.
Published: (2022)
by: Rot, Miha, et al.
Published: (2022)
Data-driven approaches to inverse problems
by: Schönlieb, Carola-Bibiane, et al.
Published: (2025)
by: Schönlieb, Carola-Bibiane, et al.
Published: (2025)
Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation
by: Benitez, J. Antonio Lara, et al.
Published: (2023)
by: Benitez, J. Antonio Lara, et al.
Published: (2023)
Numerical approximation of a class of constrained Hamilton-Jacobi equations
by: Gaudeul, Benoît, et al.
Published: (2024)
by: Gaudeul, Benoît, et al.
Published: (2024)
Orthogonal greedy algorithm for linear operator learning with shallow neural network
by: Lin, Ye, et al.
Published: (2025)
by: Lin, Ye, et al.
Published: (2025)
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors
by: Luo, Dingcheng, et al.
Published: (2025)
by: Luo, Dingcheng, et al.
Published: (2025)
HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
by: Zheng, Haoyang, et al.
Published: (2023)
by: Zheng, Haoyang, et al.
Published: (2023)
A score-based particle method for homogeneous Landau equation
by: Huang, Yan, et al.
Published: (2024)
by: Huang, Yan, et al.
Published: (2024)
Pseudo-Hamiltonian neural networks for learning partial differential equations
by: Eidnes, Sølve, et al.
Published: (2023)
by: Eidnes, Sølve, et al.
Published: (2023)
fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions
by: Aghaei, Alireza Afzal
Published: (2024)
by: Aghaei, Alireza Afzal
Published: (2024)
Estimation of instrument and noise parameters for inverse problem based on prior diffusion model
by: Giovannelli, Jean-François
Published: (2026)
by: Giovannelli, Jean-François
Published: (2026)
Similar Items
-
Momentum-based minimization of the Ginzburg-Landau functional on Euclidean spaces and graphs
by: Akande, Oluwatosin, et al.
Published: (2024) -
Deep Nonnegative Matrix Factorization with Beta Divergences
by: Leplat, Valentin, et al.
Published: (2023) -
Fast-forwarding quantum algorithms for linear dissipative differential equations
by: An, Dong, et al.
Published: (2024) -
Smoothed Moreau-Yosida Tensor Train Approximation of State-constrained Optimization Problems under Uncertainty
by: Antil, Harbir, et al.
Published: (2023) -
Neural Hamilton--Jacobi Characteristic Flows for Optimal Transport
by: Park, Yesom, et al.
Published: (2025)