Copertina

Diffeomorphism Neural Operator for various domains and parameters of partial differential equations

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Zhao, Zhiwei, Liu, Changqing, Li, Yingguang, Chen, Zhibin, Liu, Xu
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Numerical Analysis Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

Symmetry group based domain decomposition to enhance physics-informed neural networks for solving partial differential equations
di: Liu, Ye, et al.
Pubblicazione: (2024)

Neural Diffeomorphic-Neural Operator for Residual Stress-Induced Deformation Prediction
di: Liu, Changqing, et al.
Pubblicazione: (2025)

Deep Neural networks for solving high-dimensional parabolic partial differential equations
di: Zhang, Wenzhong, et al.
Pubblicazione: (2026)

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

Pseudo-Hamiltonian neural networks for learning partial differential equations
di: Eidnes, Sølve, et al.
Pubblicazione: (2023)

Sequential-in-time training of nonlinear parametrizations for solving time-dependent partial differential equations
di: Zhang, Huan, et al.
Pubblicazione: (2024)

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

Adaptation of uncertainty-penalized Bayesian information criterion for parametric partial differential equation discovery
di: Thanasutives, Pongpisit, et al.
Pubblicazione: (2024)

A shallow physics-informed neural network for solving partial differential equations on surfaces
di: Hu, Wei-Fan, et al.
Pubblicazione: (2022)

Spectral Audit of In-Context Operator Networks
di: Gao, Zhiwei, et al.
Pubblicazione: (2026)

Structure-preserving Lift & Learn: Scientific machine learning for nonlinear conservative partial differential equations
di: Sharma, Harsh, et al.
Pubblicazione: (2025)

Single-shot prediction of parametric partial differential equations
di: Rafiq, Khalid, et al.
Pubblicazione: (2025)

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

Learning the boundary-to-domain mapping using Lifting Product Fourier Neural Operators for partial differential equations
di: Kashi, Aditya, et al.
Pubblicazione: (2024)

Physics-constrained robust learning of open-form partial differential equations from limited and noisy data
di: Du, Mengge, et al.
Pubblicazione: (2023)

Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion
di: Neufeld, Ariel, et al.
Pubblicazione: (2024)

Neural network-driven domain decomposition for efficient solutions to the Helmholtz equation
di: Dolean, Victorita, et al.
Pubblicazione: (2025)

Space-time deep neural network approximations for high-dimensional partial differential equations
di: Hornung, Fabian, et al.
Pubblicazione: (2020)

Physics-informed fine-tuning of foundation models for partial differential equations
di: Medvedev, Vlad, et al.
Pubblicazione: (2026)

CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations
di: Berman, Jules, et al.
Pubblicazione: (2024)

Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
di: Li, Zongyi, et al.
Pubblicazione: (2022)

A Discrete Neural Operator with Adaptive Sampling for Surrogate Modeling of Parametric Transient Darcy Flows in Porous Media
di: Chen, Zhenglong, et al.
Pubblicazione: (2025)

Neural Operator: Learning Maps Between Function Spaces
di: Kovachki, Nikola, et al.
Pubblicazione: (2021)

Learning by solving differential equations
di: Dherin, Benoit, et al.
Pubblicazione: (2025)

Diffeomorphic Neural Operator Learning
di: Taylor, Seth, et al.
Pubblicazione: (2025)

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

Newton Informed Neural Operator for Computing Multiple Solutions of Nonlinear Partials Differential Equations
di: Hao, Wenrui, et al.
Pubblicazione: (2024)

Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
di: McGreivy, Nick, et al.
Pubblicazione: (2024)

Robust identifiability for symbolic recovery of differential equations
di: Hauger, Hillary, et al.
Pubblicazione: (2024)

MgNO: Efficient Parameterization of Linear Operators via Multigrid
di: He, Juncai, et al.
Pubblicazione: (2023)

Neural network-enhanced integrators for simulating ordinary differential equations
di: Othmane, Amine, et al.
Pubblicazione: (2025)

Universal Approximation of Operators with Transformers and Neural Integral Operators
di: Zappala, Emanuele, et al.
Pubblicazione: (2024)

MODNO: Multi Operator Learning With Distributed Neural Operators
di: Zhang, Zecheng
Pubblicazione: (2024)

Variational formulation based on duality to solve partial differential equations: Use of B-splines and machine learning approximants
di: Sukumar, N., et al.
Pubblicazione: (2024)

Continuum Attention for Neural Operators
di: Calvello, Edoardo, et al.
Pubblicazione: (2024)

Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach
di: Fang, Zhiwei, et al.
Pubblicazione: (2023)

Component Fourier Neural Operator for Singularly Perturbed Differential Equations
di: Li, Ye, et al.
Pubblicazione: (2024)

Operator learning for hyperbolic partial differential equations
di: Wang, Christopher, et al.
Pubblicazione: (2023)

Breakeven complexity: A new perspective on neural partial differential equation solvers
di: Zhang, Yijing, et al.
Pubblicazione: (2026)