:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autores principales:	Akande, Oluwatosin, Langlois, Gabriel P., Onwunta, Akwum
Formato:	Preprint
Publicado:	2025
Materias:	Numerical Analysis Machine Learning
Acceso en línea:	https://arxiv.org/abs/2512.23829
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Momentum-based minimization of the Ginzburg-Landau functional on Euclidean spaces and graphs
por: Akande, Oluwatosin, et al.
Publicado: (2024)

Deep Nonnegative Matrix Factorization with Beta Divergences
por: Leplat, Valentin, et al.
Publicado: (2023)

Fast-forwarding quantum algorithms for linear dissipative differential equations
por: An, Dong, et al.
Publicado: (2024)

Smoothed Moreau-Yosida Tensor Train Approximation of State-constrained Optimization Problems under Uncertainty
por: Antil, Harbir, et al.
Publicado: (2023)

Neural Hamilton--Jacobi Characteristic Flows for Optimal Transport
por: Park, Yesom, et al.
Publicado: (2025)

Adaptive operator learning for infinite-dimensional Bayesian inverse problems
por: Gao, Zhiwei, et al.
Publicado: (2023)

Convex-concave splitting for the Allen-Cahn equation leads to $\varepsilon^2$-slow movement of interfaces
por: Dondl, Patrick, et al.
Publicado: (2025)

DeepONet-accelerated Bayesian inversion for moving boundary problems
por: Iglesias, Marco A., et al.
Publicado: (2025)

Tensor train solution to uncertain optimization problems with shared sparsity penalty
por: Antil, Harbir, et al.
Publicado: (2024)

Deep set based operator learning with uncertainty quantification
por: Ma, Lei, et al.
Publicado: (2025)

Learning truly monotone operators with applications to nonlinear inverse problems
por: Belkouchi, Younes, et al.
Publicado: (2024)

Optimal deep learning of holomorphic operators between Banach spaces
por: Adcock, Ben, et al.
Publicado: (2024)

Derivative-informed neural operator acceleration of geometric MCMC for infinite-dimensional Bayesian inverse problems
por: Cao, Lianghao, et al.
Publicado: (2024)

Variational operator learning: A unified paradigm marrying training neural operators and solving partial differential equations
por: Xu, Tengfei, et al.
Publicado: (2023)

Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
por: Zhang, Benjamin J., et al.
Publicado: (2025)

The learned range test method for the inverse inclusion problem
por: Sun, Shiwei, et al.
Publicado: (2024)

Error analysis for finite element operator learning methods for solving parametric second-order elliptic PDEs
por: Hong, Youngjoon, et al.
Publicado: (2024)

Physics-aware deep learning framework for the limited aperture inverse obstacle scattering problem
por: Yin, Yunwen, et al.
Publicado: (2024)

Meshless solutions of PDE inverse problems on irregular geometries
por: Roggeveen, James V., et al.
Publicado: (2025)

Smoothed Moreau‐Yosida Tensor‐Train Approximation of State‐Constrained Optimization Problems Under Uncertainty
por: Harbir Antil, et al.
Publicado: (2025)

Nonlinear model reduction for operator learning
por: Eivazi, Hamidreza, et al.
Publicado: (2024)

Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
por: Ivagnes, Anna, et al.
Publicado: (2022)

Kolmogorov Arnold Informed neural network: A physics-informed deep learning framework for solving forward and inverse problems based on Kolmogorov Arnold Networks
por: Wang, Yizheng, et al.
Publicado: (2024)

Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations
por: Jentzen, Arnulf, et al.
Publicado: (2023)

Data-driven approaches to inverse problems
por: Schönlieb, Carola-Bibiane, et al.
Publicado: (2025)

Deep learning based numerical approximation algorithms for stochastic partial differential equations
por: Beck, Christian, et al.
Publicado: (2020)

Learning cardiac activation and repolarization times with operator learning
por: Centofanti, Edoardo, et al.
Publicado: (2025)

Variationally correct operator learning: Reduced basis neural operator with a posteriori error estimation
por: Qiu, Yuan, et al.
Publicado: (2025)

Harnessing physics-informed operators for high-dimensional reliability analysis problems
por: Navaneeth, N, et al.
Publicado: (2024)

HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
por: Zheng, Haoyang, et al.
Publicado: (2023)

Meshless method stencil evaluation with machine learning
por: Rot, Miha, et al.
Publicado: (2022)

Physics-informed neural networks for operator equations with stochastic data
por: Escapil-Inchauspé, Paul, et al.
Publicado: (2022)

Dilated convolution neural operator for multiscale partial differential equations
por: Xu, Bo, et al.
Publicado: (2024)

Estimation of instrument and noise parameters for inverse problem based on prior diffusion model
por: Giovannelli, Jean-François
Publicado: (2026)

Orthogonal greedy algorithm for linear operator learning with shallow neural network
por: Lin, Ye, et al.
Publicado: (2025)

Dimension reduction for derivative-informed operator learning: An analysis of approximation errors
por: Luo, Dingcheng, et al.
Publicado: (2025)

Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation
por: Benitez, J. Antonio Lara, et al.
Publicado: (2023)

Numerical approximation of a class of constrained Hamilton-Jacobi equations
por: Gaudeul, Benoît, et al.
Publicado: (2024)

Policy iteration for nonconvex viscous Hamilton--Jacobi equations
por: Guo, Xiaoqin, et al.
Publicado: (2025)

fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions
por: Aghaei, Alireza Afzal
Publicado: (2024)