Copertina

A shallow physics-informed neural network for solving partial differential equations on surfaces

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hu, Wei-Fan, Shih, Yi-Jun, Lin, Te-Sheng, Lai, Ming-Chih
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Numerical Analysis Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

A categorical embedding discontinuity-capturing shallow neural network for anisotropic elliptic interface problems
di: Hu, Wei-Fan, et al.
Pubblicazione: (2025)

Subspace method based on neural networks for solving the partial differential equation
di: Xu, Zhaodong, et al.
Pubblicazione: (2024)

Data-integrated neural networks for solving partial differential equations
di: Zheng, Jiachun, et al.
Pubblicazione: (2025)

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

Subspace method based on neural networks for solving the partial differential equation in weak form
di: Liu, Pengyuan, et al.
Pubblicazione: (2024)

A hybrid neural-network and MAC scheme for Stokes interface problems
di: Chang, Che-Chia, et al.
Pubblicazione: (2023)

SO-PIFRNN: Self-optimization physics-informed Fourier-features randomized neural network for solving partial differential equations
di: Linghu, Jiale, et al.
Pubblicazione: (2025)

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

Boundary neuron method for solving partial differential equations
di: Lin, Ye, et al.
Pubblicazione: (2026)

Diffusion models with physics-guided inference for solving partial differential equations
di: Bing, Yi, et al.
Pubblicazione: (2026)

Physics informed learning of orthogonal features with applications in solving partial differential equations
di: Jia, Qianxing, et al.
Pubblicazione: (2026)

Rank Inspired Neural Network for solving linear partial differential equations
di: Peng, Wentao, et al.
Pubblicazione: (2025)

Numerical study on hyper parameter settings for neural network approximation to partial differential equations
di: Yang, Hee Jun, et al.
Pubblicazione: (2025)

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

IG-PINNs: Interface-gated physics-informed neural networks for solving elliptic interface problems
di: Zheng, Jiachun, et al.
Pubblicazione: (2025)

Improved randomized neural network methods with boundary processing for solving elliptic equations
di: Zhou, Huifang, et al.
Pubblicazione: (2024)

Error estimates of physics-informed neural networks for approximating Boltzmann equation
di: Abdo, Elie, et al.
Pubblicazione: (2024)

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

Deep neural network approximation for high-dimensional parabolic partial integro-differential equations
di: Baranek, Marcin
Pubblicazione: (2025)

High-dimensional approximation spaces of artificial neural networks and applications to partial differential equations
di: Beneventano, Pierfrancesco, et al.
Pubblicazione: (2020)

Adaptive neural network basis methods for partial differential equations with low-regular solutions
di: Huang, Jianguo, et al.
Pubblicazione: (2024)

Moving sample method for solving time-dependent partial differential equations
di: Xu, Beining, et al.
Pubblicazione: (2026)

Pseudo-differential-enhanced physics-informed neural networks
di: Gracyk, Andrew
Pubblicazione: (2026)

A novel number-theoretic sampling method for neural network solutions of partial differential equations
di: Yang, Yu, et al.
Pubblicazione: (2024)

A backward Monte-Carlo method for solving parabolic partial differential equations
di: Carlsson, Johan
Pubblicazione: (2000)

Combining physics-informed graph neural network and finite difference for solving forward and inverse spatiotemporal PDEs
di: Zhang, Hao, et al.
Pubblicazione: (2024)

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

Adaptive feature capture method for solving partial differential equations with near singular solutions
di: Deng, Yangtao, et al.
Pubblicazione: (2025)

A piecewise neural network method for solving large interval solution to initial value problem of ordinary differential equations
di: Han, Dongpeng, et al.
Pubblicazione: (2024)

HANN: Homotopy auxiliary neural network for solving nonlinear algebraic equations
di: Zai, Ling-Zhe, et al.
Pubblicazione: (2025)

Low-rank adaptive physics-informed HyperDeepONets for solving differential equations
di: Zeudong, Etienne, et al.
Pubblicazione: (2025)

Annealed adaptive importance sampling method in PINNs for solving high dimensional partial differential equations
di: Zhang, Zhengqi, et al.
Pubblicazione: (2024)

A power series method for solving ordinary and partial differentials equations motivated by domain growth
di: Ross, Robert
Pubblicazione: (2019)

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

Regularity and error estimates in physics-informed neural networks for the Kuramoto-Sivashinsky equation
di: Rahman, Mohammad Mahabubur, et al.
Pubblicazione: (2025)

A note on the adjoint method for neural ordinary differential equation network
di: Hu, Pipi
Pubblicazione: (2024)

Physics-informed neural networks for solving two-phase flow problems with moving interfaces
di: Zhai, Qijia, et al.
Pubblicazione: (2026)

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

Spectral coefficient learning physics informed neural network for time-dependent fractional parametric differential problems
di: Sivalingam, S M, et al.
Pubblicazione: (2025)

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