Cover Image

Towards optimal hierarchical training of neural networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Feischl, Michael, Rieder, Alexander, Zehetgruber, Fabian
Format:	Preprint
Published:	2024
Subjects:	Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Computational Math with Neural Networks is Hard
by: Feischl, Michael, et al.
Published: (2025)

Optimal adaptive implicit time stepping
by: Feischl, Michael, et al.
Published: (2025)

A $p$-version of convolution quadrature in wave propagation
by: Rieder, Alexander
Published: (2024)

A Reduced Basis Method for the Stochastic Landau-Lifshitz-Gilbert Equation
by: Scaglioni, Andrea, et al.
Published: (2025)

Regularized dynamical parametric approximation
by: Feischl, Michael, et al.
Published: (2024)

Adaptive mesh refinement for the Landau-Lifshitz-Gilbert equation
by: Bohn, Jan, et al.
Published: (2023)

Functional tensor train neural network for solving high-dimensional PDEs
by: Feng, Yani, et al.
Published: (2025)

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

Fredholm integral equations for function approximation and the training of neural networks
by: Gelß, Patrick, et al.
Published: (2023)

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

Geometrical structures of digital fluctuations in parameter space of neural networks trained with adaptive momentum optimization
by: Netay, Igor V.
Published: (2024)

Gradient-free training of recurrent neural networks
by: Bolager, Erik Lien, et al.
Published: (2024)

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems
by: Dereich, Steffen, et al.
Published: (2025)

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

Astral: training physics-informed neural networks with error majorants
by: Fanaskov, Vladimir, et al.
Published: (2024)

Stable decompositions of $hp$-BEM spaces and an optimal Schwarz preconditioner for the hypersingular integral operator in 3D
by: Karkulik, Michael, et al.
Published: (2018)

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

Multi-Fidelity Delayed Acceptance: hierarchical MCMC sampling for Bayesian inverse problems combining multiple solvers through deep neural networks
by: Zacchei, Filippo, et al.
Published: (2025)

Two-scale neural networks for optimal control of linear convection-dominated equations
by: Liu, Sijing, et al.
Published: (2026)

A neural network kernel decomposition for learning multiple steady states in parameterized dynamical systems
by: Zhang, Yimeng, et al.
Published: (2023)

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

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

Volume-preserving geometric shape optimization of the Dirichlet energy using variational neural networks
by: Bélières--Frendo, Amaury, et al.
Published: (2024)

On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks
by: Yang, Yunfei
Published: (2024)

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

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

Gradient-free neural topology optimization: Towards effective fracture-resistant designs
by: Kus, Gawel, et al.
Published: (2024)

A neural network approach to learning solutions of a class of elliptic variational inequalities
by: Alphonse, Amal, et al.
Published: (2024)

A decomposition-based robust training of physics-informed neural networks for nearly incompressible linear elasticity
by: Dick, Josef, et al.
Published: (2025)

Physics-informed neural networks for Timoshenko system with Thermoelasticity
by: Chebbi, Sabrine, et al.
Published: (2024)

Metastable Speeds in the Fractional Allen-Cahn Equation
by: Achleitner, Franz, et al.
Published: (2020)

Sensitivity analysis using Physics-informed neural networks
by: Hanna, John M., et al.
Published: (2023)

Adaptive optimization of isogeometric multi-patch discretizations using artificial neural networks
by: Rios, Dany, et al.
Published: (2024)

Tensor neural networks for high-dimensional Fokker-Planck equations
by: Wang, Taorui, et al.
Published: (2024)

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

Artificial neural network solver for Fokker-Planck and Koopman eigenfunctions
by: Kreider, Max, et al.
Published: (2025)

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

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

Artificial neural network evaluation of geometric constants for polygonal domains
by: Crippa, Beatrice, et al.
Published: (2022)

Deep neural network approximation theory for high-dimensional functions
by: Beneventano, Pierfrancesco, et al.
Published: (2021)