Cover Image

Data-Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Alegría, Samuel A. Cruz, Trotti, Ken, Kopaničáková, Alena, Krause, Rolf
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Numerical Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Trust-Region Methods with Low-Fidelity Objective Models
by: Angino, Andrea, et al.
Published: (2025)

Multi-Preconditioned LBFGS for Training Finite-Basis PINNs
by: Salvadó-Benasco, Marc, et al.
Published: (2026)

An Additively Preconditioned Trust Region Strategy for Machine Learning
by: Alegría, Samuel Cruz, et al.
Published: (2025)

A Non-Monotone Preconditioned Trust-Region Method for Neural Network Training
by: Angino, Andrea, et al.
Published: (2026)

Nonlinear Schwarz preconditioning for nonlinear optimization problems with bound constraints
by: Kothari, Hardik, et al.
Published: (2022)

Two-level trust-region method with random subspaces
by: Angino, Andrea, et al.
Published: (2024)

Leveraging Operator Learning to Accelerate Convergence of the Preconditioned Conjugate Gradient Method
by: Kopaničáková, Alena, et al.
Published: (2025)

Blending Neural Operators and Relaxation Methods in PDE Numerical Solvers
by: Zhang, Enrui, et al.
Published: (2022)

DeepOnet Based Preconditioning Strategies For Solving Parametric Linear Systems of Equations
by: Kopaničáková, Alena, et al.
Published: (2024)

Preconditioning for Physics-Informed Neural Networks
by: Liu, Songming, et al.
Published: (2024)

The limitations of a standard phase-field model in reproducing jointing in sedimentary rock layers
by: Pezzulli, Edoardo, et al.
Published: (2024)

Physics-Informed Neural Networks with Trust-Region Sequential Quadratic Programming
by: Cheng, Xiaoran, et al.
Published: (2024)

Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies
by: Kopaničáková, Alena, et al.
Published: (2023)

Solving Roughly Forced Nonlinear PDEs via Misspecified Kernel Methods and Neural Networks
by: Baptista, Ricardo, et al.
Published: (2025)

Parallel-in-Time Solutions with Random Projection Neural Networks
by: Betcke, Marta M., et al.
Published: (2024)

Parareal Neural Networks Emulating a Parallel-in-time Algorithm
by: Lee, Chang-Ock, et al.
Published: (2021)

Introduction to optimization methods for training SciML models
by: Kopaničáková, Alena, et al.
Published: (2026)

Preconditioned FEM-based Neural Networks for Solving Incompressible Fluid Flows and Related Inverse Problems
by: Griese, Franziska, et al.
Published: (2024)

Revisiting Orbital Minimization Method for Neural Operator Decomposition
by: Ryu, J. Jon, et al.
Published: (2025)

Preconditioned Additive Gaussian Processes with Fourier Acceleration
by: Wagner, Theresa, et al.
Published: (2025)

Preconditioning for Accelerated Gradient Descent Optimization and Regularization
by: Ye, Qiang
Published: (2024)

Supervised and Unsupervised Neural Network Solver for First Order Hyperbolic Nonlinear PDEs
by: Baba, Zakaria, et al.
Published: (2026)

A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Method
by: Fang, Yuchen, et al.
Published: (2026)

Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
by: Chen, Chuqi, et al.
Published: (2024)

One-Shot Transfer Learning for Nonlinear PDEs with Perturbative PINNs
by: Auroy, Samuel, et al.
Published: (2025)

HyperNOs: Automated and Parallel Library for Neural Operators Research
by: Ghiotto, Massimiliano
Published: (2025)

Quantifying Training Difficulty and Accelerating Convergence in Neural Network-Based PDE Solvers
by: Chen, Chuqi, et al.
Published: (2024)

$ε$-rank and the Staircase Phenomenon: New Insights into Neural Network Training Dynamics
by: Yang, Jiang, et al.
Published: (2024)

Pychop: Emulating Low-Precision Arithmetic in Numerical Methods and Neural Networks
by: Carson, Erin, et al.
Published: (2025)

Provable Emergence of Deep Neural Collapse and Low-Rank Bias in $L^2$-Regularized Nonlinear Networks
by: Zangrando, Emanuele, et al.
Published: (2024)

Nonlocality and Nonlinearity Implies Universality in Operator Learning
by: Lanthaler, Samuel, et al.
Published: (2023)

Preconditioned One-Step Generative Modeling for Bayesian Inverse Problems in Function Spaces
by: Cheng, Zilan, et al.
Published: (2026)

Coupling-Robust Accuracy in Multiphysics Physics Informed Neural Networks via Kronecker-Preconditioned Optimization
by: Park, Youngjae, et al.
Published: (2026)

Affine Invariant Ensemble Transform Methods to Improve Predictive Uncertainty in Neural Networks
by: Bhandari, Diksha, et al.
Published: (2023)

eXtended Physics Informed Neural Network Method for Fracture Mechanics Problems
by: Lotfalian, Amin, et al.
Published: (2025)

Quantitative Approximation for Neural Operators in Nonlinear Parabolic Equations
by: Furuya, Takashi, et al.
Published: (2024)

Adaptive-Growth Randomized Neural Networks for Level-Set Computation of Multivalued Nonlinear First-Order PDEs with Hyperbolic Characteristics
by: Dang, Haoning, et al.
Published: (2026)

A PDE-based Explanation of Extreme Numerical Sensitivities and Edge of Stability in Training Neural Networks
by: Sun, Yuxin, et al.
Published: (2022)

Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
by: Jin, Bangti, et al.
Published: (2025)

A Multiple Transferable Neural Network Method with Domain Decomposition for Elliptic Interface Problems
by: Lu, Tianzheng, et al.
Published: (2025)