Enregistré dans:
| Auteurs principaux: | Nooraiepour, Mohammad, Both, Jakub Wiktor, Kadeethum, Teeratorn, Sadeghnejad, Saeid |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2603.07655 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
Well-posedness analysis of the Cahn-Hilliard-Biot model
par: Riethmüller, Cedric, et autres
Publié: (2023)
par: Riethmüller, Cedric, et autres
Publié: (2023)
Double Coupling Architecture and Training Method for Optimization Problems of Differential Algebraic Equations with Parameters
par: Yang, Wenqiang, et autres
Publié: (2026)
par: Yang, Wenqiang, et autres
Publié: (2026)
BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
par: Mirzabeigi, Elmira, et autres
Publié: (2025)
par: Mirzabeigi, Elmira, et autres
Publié: (2025)
Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches
par: Wang, Yixuan
Publié: (2026)
par: Wang, Yixuan
Publié: (2026)
Wavelet-Accelerated Physics-Informed Quantum Neural Network for Multiscale Partial Differential Equations
par: Gupta, Deepak, et autres
Publié: (2025)
par: Gupta, Deepak, et autres
Publié: (2025)
Meta-learning Loss Functions of Parametric Partial Differential Equations Using Physics-Informed Neural Networks
par: Koumpanakis, Michail, et autres
Publié: (2024)
par: Koumpanakis, Michail, et autres
Publié: (2024)
Exact Solutions for Nonlinear Partial Differential Equations: A Fusion of Classical Methods and Innovative Approaches
par: Mhadhbi, Noureddine, et autres
Publié: (2023)
par: Mhadhbi, Noureddine, et autres
Publié: (2023)
An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
par: Gonon, Lukas, et autres
Publié: (2024)
par: Gonon, Lukas, et autres
Publié: (2024)
A Machine Learning Approach to the Nirenberg Problem
par: Cortés, Gianfranco, et autres
Publié: (2026)
par: Cortés, Gianfranco, et autres
Publié: (2026)
Variational (Energy-Based) Spectral Learning: A Machine Learning Framework for Solving Partial Differential Equations
par: Hammad, M. M.
Publié: (2026)
par: Hammad, M. M.
Publié: (2026)
Resolving Sharp Gradients of Unstable Singularities to Machine Precision via Neural Networks
par: Wang, Yongji, et autres
Publié: (2025)
par: Wang, Yongji, et autres
Publié: (2025)
Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
par: Andre-Sloan, Sebastien, et autres
Publié: (2026)
par: Andre-Sloan, Sebastien, et autres
Publié: (2026)
Stochastic Langevin Differential Inclusions with Applications to Machine Learning
par: Difonzo, Fabio V., et autres
Publié: (2022)
par: Difonzo, Fabio V., et autres
Publié: (2022)
Continuation for Nonlinear Elliptic Partial Differential Equations Discretized by the Multiquadric Method
par: Fedoseyev, A. I., et autres
Publié: (1998)
par: Fedoseyev, A. I., et autres
Publié: (1998)
Variational Physics-informed Neural Operator (VINO) for Solving Partial Differential Equations
par: Eshaghi, Mohammad Sadegh, et autres
Publié: (2024)
par: Eshaghi, Mohammad Sadegh, et autres
Publié: (2024)
Is Zero-Shot Super-Resolution Possible in Operator Learning?
par: Subedi, Unique, et autres
Publié: (2026)
par: Subedi, Unique, et autres
Publié: (2026)
Stiff Transfer Learning for Physics-Informed Neural Networks
par: Seiler, Emilien, et autres
Publié: (2025)
par: Seiler, Emilien, et autres
Publié: (2025)
Anisotropic Permeability Tensor Prediction from Porous Media Microstructure via Physics-Informed Progressive Transfer Learning with Hybrid CNN-Transformer
par: Nooraiepour, Mohammad
Publié: (2026)
par: Nooraiepour, Mohammad
Publié: (2026)
Solving the Poisson Equation with Dirichlet data by shallow ReLU$^α$-networks: A regularity and approximation perspective
par: Vaishampayan, Malhar, et autres
Publié: (2024)
par: Vaishampayan, Malhar, et autres
Publié: (2024)
H-DES: a Quantum-Classical Hybrid Differential Equation Solver
par: Jaffali, Hamza, et autres
Publié: (2024)
par: Jaffali, Hamza, et autres
Publié: (2024)
Learning functional components of PDEs from data using neural networks
par: Loman, Torkel E., et autres
Publié: (2026)
par: Loman, Torkel E., et autres
Publié: (2026)
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input
par: Chen, Ziang, et autres
Publié: (2024)
par: Chen, Ziang, et autres
Publié: (2024)
Deep Learning-Enhanced Calibration of the Heston Model: A Unified Framework
par: Zadgar, Arman, et autres
Publié: (2025)
par: Zadgar, Arman, et autres
Publié: (2025)
Generalization Error Bounds for Picard-Type Operator Learning in Nonlinear Parabolic PDEs
par: Taniguchi, Koichi, et autres
Publié: (2026)
par: Taniguchi, Koichi, et autres
Publié: (2026)
Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
par: Chen, Jiale, et autres
Publié: (2024)
par: Chen, Jiale, et autres
Publié: (2024)
Efficient Numerical Wave Propagation Enhanced By An End-to-End Deep Learning Model
par: Kaiser, Luis, et autres
Publié: (2024)
par: Kaiser, Luis, et autres
Publié: (2024)
Scale-Consistent Learning for Partial Differential Equations
par: Li, Zongyi, et autres
Publié: (2025)
par: Li, Zongyi, et autres
Publié: (2025)
Learning PDE Solvers with Physics and Data: A Unifying View of Physics-Informed Neural Networks and Neural Operators
par: Dai, Yilong, et autres
Publié: (2026)
par: Dai, Yilong, et autres
Publié: (2026)
Bayesian Parametric Matrix Models: Principled Uncertainty Quantification for Spectral Learning
par: Nooraiepour, Mohammad
Publié: (2025)
par: Nooraiepour, Mohammad
Publié: (2025)
A Hybrid Kernel-Free Boundary Integral Method with Operator Learning for Solving Parametric Partial Differential Equations In Complex Domains
par: Ling, Shuo, et autres
Publié: (2024)
par: Ling, Shuo, et autres
Publié: (2024)
Soliton profiles: Classical Numerical Schemes vs. Neural Network - Based Solvers
par: Haight, Chandler, et autres
Publié: (2025)
par: Haight, Chandler, et autres
Publié: (2025)
Score Shocks: The Burgers Equation Structure of Diffusion Generative Models
par: Sarkar, Krisanu
Publié: (2026)
par: Sarkar, Krisanu
Publié: (2026)
Latent Space Element Method
par: Chung, Seung Whan, et autres
Publié: (2026)
par: Chung, Seung Whan, et autres
Publié: (2026)
Structure-informed operator learning for parabolic Partial Differential Equations
par: Benth, Fred Espen, et autres
Publié: (2024)
par: Benth, Fred Espen, et autres
Publié: (2024)
An Orthogonal Polynomial Kernel-Based Machine Learning Model for Differential-Algebraic Equations
par: Taheri, Tayebeh, et autres
Publié: (2024)
par: Taheri, Tayebeh, et autres
Publié: (2024)
SPDEBench: An Extensive Benchmark for Learning Stochastic PDEs
par: Zhu, Yuantu, et autres
Publié: (2025)
par: Zhu, Yuantu, et autres
Publié: (2025)
Self-Supervised Learning with Lie Symmetries for Partial Differential Equations
par: Mialon, Grégoire, et autres
Publié: (2023)
par: Mialon, Grégoire, et autres
Publié: (2023)
Learning embeddings of non-linear PDEs: the Burgers' equation
par: Tarancón-Álvarez, Pedro, et autres
Publié: (2026)
par: Tarancón-Álvarez, Pedro, et autres
Publié: (2026)
Variational Quantum Framework for Partial Differential Equation Constrained Optimization
par: Surana, Amit, et autres
Publié: (2024)
par: Surana, Amit, et autres
Publié: (2024)
Predicting Peak Stresses In Microstructured Materials Using Convolutional Encoder-Decoder Learning
par: Shrivastava, Ankit, et autres
Publié: (2022)
par: Shrivastava, Ankit, et autres
Publié: (2022)
Documents similaires
-
Well-posedness analysis of the Cahn-Hilliard-Biot model
par: Riethmüller, Cedric, et autres
Publié: (2023) -
Double Coupling Architecture and Training Method for Optimization Problems of Differential Algebraic Equations with Parameters
par: Yang, Wenqiang, et autres
Publié: (2026) -
BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck Equations
par: Mirzabeigi, Elmira, et autres
Publié: (2025) -
Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches
par: Wang, Yixuan
Publié: (2026) -
Wavelet-Accelerated Physics-Informed Quantum Neural Network for Multiscale Partial Differential Equations
par: Gupta, Deepak, et autres
Publié: (2025)