Copertina

Sensitivity analysis using Physics-informed neural networks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Hanna, John M., Aguado, José V., Comas-Cardona, Sebastien, Askri, Ramzi, Borzacchiello, Domenico
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Numerical Analysis
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Sparse approximations for contact mechanics
di: Kollepara, Kiran Sagar, et al.
Pubblicazione: (2024)

Physics-informed neural networks for Timoshenko system with Thermoelasticity
di: Chebbi, Sabrine, 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)

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

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

Physics informed neural network for forward and inverse radiation heat transfer in graded-index medium
di: Murari, K., et al.
Pubblicazione: (2024)

Multiprecision computing for multistage fractional physics-informed neural networks
di: Xue, Na, et al.
Pubblicazione: (2025)

Weak and entropy physics-informed neural networks for conservation laws
di: Oubarka, Ismail, et al.
Pubblicazione: (2026)

Multilevel domain decomposition-based architectures for physics-informed neural networks
di: Dolean, Victorita, et al.
Pubblicazione: (2023)

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

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

An extended physics informed neural network for preliminary analysis of parametric optimal control problems
di: Demo, Nicola, et al.
Pubblicazione: (2021)

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

Physics-informed neural networks (PINNs) for numerical model error approximation and superresolution
di: Zhuang, Bozhou, et al.
Pubblicazione: (2024)

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

Why Cannot Neural Networks Master Extrapolation? Insights from Physical Laws
di: Dakhmouche, Ramzi, et al.
Pubblicazione: (2025)

Exact and approximate error bounds for physics-informed neural networks
di: Chantada, Augusto T., et al.
Pubblicazione: (2024)

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

A residual weighted physics informed neural network for forward and inverse problems of reaction diffusion equations
di: Murari, K., et al.
Pubblicazione: (2025)

Solving engineering eigenvalue problems with neural networks using the Rayleigh quotient
di: Rowan, Conor, et al.
Pubblicazione: (2025)

Some aspects of neural network parameter optimization for joint inversion of gravitational and magnetic fields
di: Wang, Yanfei, et al.
Pubblicazione: (2025)

Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning
di: De Ryck, Tim, et al.
Pubblicazione: (2024)

Domain decomposition architectures and Gauss-Newton training for physics-informed neural networks
di: Heinlein, Alexander, et al.
Pubblicazione: (2025)

Adaptive quadratures for nonlinear approximation of low-dimensional PDEs using smooth neural networks
di: Magueresse, Alexandre, et al.
Pubblicazione: (2023)

Hamiltonian reduction using a convolutional auto-encoder coupled to an Hamiltonian neural network
di: Côte, Raphaël, et al.
Pubblicazione: (2023)

A new variable shape parameter strategy for RBF approximation using neural networks
di: Mojarrad, Fatemeh Nassajian, et al.
Pubblicazione: (2022)

Long-term simulation of physical and mechanical behaviors using curriculum-transfer-learning based physics-informed neural networks
di: Guo, Yuan, et al.
Pubblicazione: (2025)

On understanding and overcoming spectral biases of deep neural network learning methods for solving PDEs
di: Xu, Zhi-Qin John, et al.
Pubblicazione: (2025)

Optimal time sampling in physics-informed neural networks
di: Turinici, Gabriel
Pubblicazione: (2024)

Physics-informed reduced order model with conditional neural fields
di: Kim, Minji, et al.
Pubblicazione: (2024)

Compatible finite element interpolated neural networks
di: Badia, Santiago, et al.
Pubblicazione: (2024)

Towards optimal hierarchical training of neural networks
di: Feischl, Michael, et al.
Pubblicazione: (2024)

Unfitted finite element interpolated neural networks
di: Li, Wei, et al.
Pubblicazione: (2025)

Certified machine learning: A posteriori error estimation for physics-informed neural networks
di: Hillebrecht, Birgit, et al.
Pubblicazione: (2022)

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

A matrix preconditioning framework for physics-informed neural networks based on adjoint method
di: Song, Jiahao, et al.
Pubblicazione: (2025)

Conservative semi-lagrangian finite difference scheme for transport simulations using graph neural networks
di: Chen, Yongsheng, et al.
Pubblicazione: (2024)

Physics-informed neural networks and neural operators for a study of EUV electromagnetic wave diffraction from a lithography mask
di: Es'kin, Vasiliy A., et al.
Pubblicazione: (2025)

Fully discrete analysis of the Galerkin POD neural network approximation with application to 3D acoustic wave scattering
di: Dölz, Jürgen, et al.
Pubblicazione: (2025)

A physics-informed neural network framework for modeling obstacle-related equations
di: Bahja, Hamid El, et al.
Pubblicazione: (2023)