Image de couverture de livre

Numerical analysis of physics-informed neural networks and related models in physics-informed machine learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	De Ryck, Tim, Mishra, Siddhartha
Format:	Preprint
Publié:	2024
Sujets:	Numerical Analysis Machine Learning 65M15
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Improving the accuracy of physics-informed neural networks via last-layer retraining
par: Qadeer, Saad, et autres
Publié: (2026)

Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems
par: Heinlein, Alexander, et autres
Publié: (2024)

A posteriori analysis of neural network approximations
par: Führer, Thomas, et autres
Publié: (2025)

Numerical analysis of a stabilized scheme for an optimal control problem governed by a parabolic convection--diffusion equation
par: Pervolianakis, Christos
Publié: (2024)

Mitigating distribution shift in machine learning-augmented hybrid simulation
par: Zhao, Jiaxi, et autres
Publié: (2024)

The Numerical Solution in the Sense of Prager&Synge
par: Alekseev, A. K., et autres
Publié: (2024)

Certified machine learning: A posteriori error estimation for physics-informed neural networks
par: Hillebrecht, Birgit, et autres
Publié: (2022)

Enforcing boundary conditions for physics-informed neural operators
par: Göschel, Niklas, et autres
Publié: (2025)

A physics-informed neural network method for the approximation of slow invariant manifolds for the general class of stiff systems of ODEs
par: Patsatzis, Dimitrios G., et autres
Publié: (2024)

An operator preconditioning perspective on training in physics-informed machine learning
par: De Ryck, Tim, et autres
Publié: (2023)

Error analysis for learning the time-stepping operator of evolutionary PDEs
par: Chen, Ke, et autres
Publié: (2025)

Regularized dynamical parametric approximation of stiff evolution problems
par: Lubich, Christian, et autres
Publié: (2025)

A Theory-guided Weighted $L^2$ Loss for solving the BGK model via Physics-informed neural networks
par: Ko, Gyounghun, et autres
Publié: (2026)

An FDM-sFEM scheme on time-space manifolds and its superconvergence analysis
par: Jiang, Chengrun, et autres
Publié: (2025)

PINN-DG: Residual neural network methods trained with Finite Elements
par: Grekas, Georgios, et autres
Publié: (2025)

Stabilized neural Hamilton--Jacobi--Bellman solvers: Error analysis and applications in model-based reinforcement learning
par: Kim, Minseok, et autres
Publié: (2026)

Error analysis of a backward Euler positive preserving stabilized scheme for a Chemotaxis system
par: Chatzipantelidis, Panagiotis, et autres
Publié: (2022)

Unconditionally stable second order convergent partitioned methods for multiple-network poroelasticity
par: Lee, Jeonghun J.
Publié: (2019)

Error analysis of an Algebraic Flux Correction Scheme for a nonlinear Scalar Conservation Law Using SSP-RK2
par: Pervolianakis, Christos
Publié: (2024)

Numerical analysis of 2D Navier--Stokes equations with nonsmooth initial value in the critical space
par: Li, Buyang, et autres
Publié: (2025)

Numerical simulation of wormhole propagation with the mixed hybridized discontinuous Galerkin finite element method
par: Zhang, Jiansong, et autres
Publié: (2025)

Entropy stable conservative flux form neural networks
par: Liu, Lizuo, et autres
Publié: (2024)

Mass lumping and stabilization for immersogeometric analysis
par: Voet, Yannis, et autres
Publié: (2025)

A neural network method for scalar conservation laws with convergence rates for shock-wave solutions
par: Cao, Jiachuan, et autres
Publié: (2026)

Conservative approximation-based feedforward neural network for WENO schemes
par: Park, Kwanghyuk, et autres
Publié: (2025)

Neural Entropy-stable conservative flux form neural networks for learning hyperbolic conservation laws
par: Liu, Lizuo, et autres
Publié: (2025)

Error analysis for discontinuous Galerkin time-stepping methods for nonlinear parabolic equations via maximal regularity
par: Akrivis, Georgios, et autres
Publié: (2024)

Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks
par: Chen, Yongsheng, et autres
Publié: (2025)

A physics-informed neural network framework for modeling obstacle-related equations
par: Bahja, Hamid El, et autres
Publié: (2023)

A theoretical study on the effect of mass lumping on the discrete frequencies in immersogeometric analysis
par: Bioli, Ivan, et autres
Publié: (2024)

Pseudo-differential-enhanced physics-informed neural networks
par: Gracyk, Andrew
Publié: (2026)

A theoretical analysis of mass scaling techniques
par: Voet, Yannis, et autres
Publié: (2024)

Do we need decay-preserving error estimate for solving parabolic equations with initial singularity?
par: Zhang, Jiwei, et autres
Publié: (2024)

A priori and a posteriori error estimates for discontinuous Galerkin time-discrete methods via maximal regularity
par: Akrivis, Georgios, et autres
Publié: (2024)

Reducing polynomial degree by one for inner-stage operators affects neither stability type nor accuracy order of the Runge--Kutta discontinuous Galerkin method
par: Sun, Zheng
Publié: (2024)

Pointwise error bounds in POD methods without difference quotients
par: García-Archilla, Bosco, et autres
Publié: (2024)

Stability and convergence of multi-product expansion splitting methods with negative weights for semilinear parabolic equations
par: Duan, Xianglong, et autres
Publié: (2025)

A Stable Loosely-Coupled Dirichlet-Neumann Scheme for Fluid-Structure Interaction with Large Added Mass
par: Renzi, Francesca, et autres
Publié: (2025)

Unconditional energy dissipation of Strang splitting for the matrix-valued Allen-Cahn equation
par: Quan, Chaoyu, et autres
Publié: (2025)

New Crouzeix-Raviart elements of even degree: theoretical aspects, numerical performance, and applications to the Stokes' equations
par: Bressan, Andrea, et autres
Publié: (2025)