Copertina

Multi-Preconditioned LBFGS for Training Finite-Basis PINNs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Salvadó-Benasco, Marc, Kssim, Aymane, Heinlein, Alexander, Krause, Rolf, Gratton, Serge, Kopaničáková, Alena
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Numerical Analysis Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Data-Parallel Neural Network Training via Nonlinearly Preconditioned Trust-Region Method
di: Alegría, Samuel A. Cruz, et al.
Pubblicazione: (2025)

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

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

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

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

Layer-Parallel Training for Transformers
di: Jiang, Shuai, et al.
Pubblicazione: (2026)

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

Towards Model Discovery Using Domain Decomposition and PINNs
di: Saha, Tirtho S., et al.
Pubblicazione: (2024)

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

RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs
di: Song, Zhenao
Pubblicazione: (2025)

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

Are Deep Learning Based Hybrid PDE Solvers Reliable? Why Training Paradigms and Update Strategies Matter
di: Wu, Yuhan, et al.
Pubblicazione: (2026)

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

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

Investigating the Ability of PINNs To Solve Burgers' PDE Near Finite-Time BlowUp
di: Kumar, Dibyakanti, et al.
Pubblicazione: (2023)

Recursive Bound-Constrained AdaGrad with Applications to Multilevel and Domain Decomposition Minimization
di: Gratton, Serge, et al.
Pubblicazione: (2025)

Faster Computation of Entropic Optimal Transport via Stable Low Frequency Modes
di: Chhaibi, Reda, et al.
Pubblicazione: (2025)

Integral regularization PINNs for evolution equations
di: Feng, Xiaodong, et al.
Pubblicazione: (2025)

Auto-Adaptive PINNs with Applications to Phase Transitions
di: Buck, Kevin, et al.
Pubblicazione: (2025)

Understanding the Difficulty of Solving Cauchy Problems with PINNs
di: Wang, Tao, et al.
Pubblicazione: (2024)

E-PINNs: Epistemic Physics-Informed Neural Networks
di: Jacob, Bruno, et al.
Pubblicazione: (2025)

Accelerating Fractional PINNs using Operational Matrices of Derivative
di: Taheri, Tayebeh, et al.
Pubblicazione: (2024)

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

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

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

Using Parametric PINNs for Predicting Internal and External Turbulent Flows
di: Ghosh, Shinjan, et al.
Pubblicazione: (2024)

TGPT-PINN: Nonlinear model reduction with transformed GPT-PINNs
di: Chen, Yanlai, et al.
Pubblicazione: (2024)

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

Preconditioning for Accelerated Gradient Descent Optimization and Regularization
di: Ye, Qiang
Pubblicazione: (2024)

Deflation-PINNs: Learning Multiple Solutions for PDEs and Landau-de Gennes
di: Disarò, Sean, et al.
Pubblicazione: (2026)

Beyond Derivative Pathology of PINNs: Variable Splitting Strategy with Convergence Analysis
di: Park, Yesom, et al.
Pubblicazione: (2024)

High precision PINNs in unbounded domains: application to singularity formulation in PDEs
di: Wang, Yixuan, et al.
Pubblicazione: (2025)

Accelerating Natural Gradient Descent for PINNs with Randomized Numerical Linear Algebra
di: Bioli, Ivan, et al.
Pubblicazione: (2025)

PINNsFailureRegion Localization and Refinement through White-box AdversarialAttack
di: Shi, Shengzhu, et al.
Pubblicazione: (2023)

Stochastic Dimension Implicit Functional Projections for Exact Integral Conservation in High-Dimensional PINNs
di: Liang, Zhangyong
Pubblicazione: (2026)

Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs
di: Hillebrecht, Birgit, et al.
Pubblicazione: (2022)

Boundary condition enforcement with PINNs: a comparative study and verification on 3D geometries
di: Rowan, Conor, et al.
Pubblicazione: (2025)

PINNs error estimates for nonlinear equations in $\mathbb{R}$-smooth Banach spaces
di: Gao, Jiexing, et al.
Pubblicazione: (2023)

SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
di: Gao, Yihang, et al.
Pubblicazione: (2022)

Don't Fix the Basis -- Learn It: Spectral Representation with Adaptive Basis Learning for PDEs
di: Zhao, Xuxiang, et al.
Pubblicazione: (2026)