Cover Image

A fast neural hybrid Newton solver adapted to implicit methods for nonlinear dynamics

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jin, Tianyu, Maierhofer, Georg, Schratz, Katharina, Xiang, Yang
Format:	Preprint
Published:	2024
Subjects:	Numerical Analysis Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Fully discrete backward error analysis for the midpoint rule applied to the nonlinear Schroedinger equation
by: Faou, Erwan, et al.
Published: (2025)

G-Adaptivity: optimised graph-based mesh relocation for finite element methods
by: Rowbottom, James, et al.
Published: (2024)

Bridging the gap: symplecticity and low regularity in Runge-Kutta resonance-based schemes
by: Maierhofer, Georg, et al.
Published: (2022)

Computing nonlinear Schrödinger equations with Hermite functions beyond harmonic traps
by: Banica, Valeria, et al.
Published: (2025)

Symmetric resonance based integrators and forest formulae
by: Bronsard, Yvonne Alama, et al.
Published: (2023)

A Wong--Zakai resonance-based integrator for nonlinear Schrödinger equation with white noise dispersion
by: Cui, Jianbo, et al.
Published: (2025)

CoLoRA: Continuous low-rank adaptation for reduced implicit neural modeling of parameterized partial differential equations
by: Berman, Jules, et al.
Published: (2024)

Energy stable neural network for gradient flow equations
by: Wu, Yue, et al.
Published: (2023)

An accelerated Levin-Clenshaw-Curtis method for the evaluation of highly oscillatory integrals
by: Iserles, Arieh, et al.
Published: (2024)

A hybrid iterative method based on MIONet for PDEs: Theory and numerical examples
by: Hu, Jun, et al.
Published: (2024)

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

KD-PINN: Knowledge-Distilled PINNs for ultra-low-latency real-time neural PDE solvers
by: Bounja, Karim, et al.
Published: (2025)

Resonances as a computational tool
by: Rousset, Frédéric, et al.
Published: (2024)

VS-PINN: A fast and efficient training of physics-informed neural networks using variable-scaling methods for solving PDEs with stiff behavior
by: Ko, Seungchan, et al.
Published: (2024)

Unified theoretical guarantees for stability, consistency, and convergence in neural PDE solvers from non-IID data to physics-informed networks
by: Katende, Ronald
Published: (2024)

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)

Single-seed generation of Brownian paths and integrals for adaptive and high order SDE solvers
by: Jelinčič, Andraž, et al.
Published: (2024)

A note on the adjoint method for neural ordinary differential equation network
by: Hu, Pipi
Published: (2024)

Stable neural networks and connections to continuous dynamical systems
by: Ehrhardt, Matthias J., et al.
Published: (2025)

Leveraging time and parameters for nonlinear model reduction methods
by: Glas, Silke, et al.
Published: (2025)

On latent dynamics learning in nonlinear reduced order modeling
by: Farenga, Nicola, et al.
Published: (2024)

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

On scattering for NLS: rigidity properties and numerical simulations via the lens transform
by: Carles, Rémi, et al.
Published: (2025)

Two-hidden-layer ReLU neural networks and finite elements
by: Jin, Pengzhan
Published: (2024)

Fixed-budget online adaptive learning for physics-informed neural networks. Towards parameterized problem inference
by: Nguyen, Thi Nguyen Khoa, et al.
Published: (2022)

A parallel-in-time Newton's method-based ODE solver
by: Iacob, Casian, et al.
Published: (2025)

Newton Informed Neural Operator for Computing Multiple Solutions of Nonlinear Partials Differential Equations
by: Hao, Wenrui, et al.
Published: (2024)

Precision autotuning for linear solvers via contextual bandit-based RL
by: Carson, Erin, et al.
Published: (2026)

On nonlinear magnetic field solvers using local Quasi-Newton updates
by: Egger, Herbert, et al.
Published: (2024)

A distributed semismooth Newton based augmented Lagrangian method for distributed optimization
by: Ma, Qihao, et al.
Published: (2026)

RGNMR: A Gauss-Newton method for robust matrix completion with theoretical guarantees
by: Laufer, Eilon Vaknin, et al.
Published: (2025)

Breakeven complexity: A new perspective on neural partial differential equation solvers
by: Zhang, Yijing, et al.
Published: (2026)

Flow-based Bayesian filtering for high-dimensional nonlinear stochastic dynamical systems
by: Wang, Xintong, et al.
Published: (2025)

NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces
by: Levrero-Florencio, Francesc, et al.
Published: (2026)

An Accelerated Newton-GMRES Method for Multilinear PageRank
by: Boubekraoui, Maryam, et al.
Published: (2025)

Efficient implicit solvers for models of neuronal networks
by: Bonaventura, Luca, et al.
Published: (2022)

Sine-transform-based fast solvers for Riesz fractional nonlinear Schrödinger equations with attractive nonlinearities
by: Chen, Chao, et al.
Published: (2024)

A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations
by: Li, Hongyan, et al.
Published: (2022)

Memorization capacity of deep ReLU neural networks characterized by width and depth
by: Yang, Xin, et al.
Published: (2026)

Accelerating the convergence of Newton's method for nonlinear elliptic PDEs using Fourier neural operators
by: Aghili, Joubine, et al.
Published: (2024)