Copertina

TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Chen, Zhuo, McCarran, Jacob, Vizcaino, Esteban, Soljačić, Marin, Luo, Di
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Numerical Analysis Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Solving PDEs With Deep Neural Nets under General Boundary Conditions
di: Zhang, Chenggong
Pubblicazione: (2025)

TENG-BC: Unified Time-Evolving Natural Gradient for Neural PDE Solvers with General Boundary Conditions
di: Jiang, Hongjie, et al.
Pubblicazione: (2026)

TINNs: Time-Induced Neural Networks for Solving Time-Dependent PDEs
di: Dai, Chen-Yang, et al.
Pubblicazione: (2026)

Latent Neural Operator Pretraining for Solving Time-Dependent PDEs
di: Wang, Tian, et al.
Pubblicazione: (2024)

Solving PDEs on Unknown Manifolds with Machine Learning
di: Liang, Senwei, et al.
Pubblicazione: (2021)

Convergence Guarantees for Gradient-Based Training of Neural PDE Solvers: From Linear to Nonlinear PDEs
di: Zhao, Wei, et al.
Pubblicazione: (2025)

Stochastic Quadrature Rules for Solving PDEs using Neural Networks
di: Taylor, Jamie M., et al.
Pubblicazione: (2025)

Solving High-Dimensional PDEs Using Linearized Neural Networks
di: Mao, Tong, et al.
Pubblicazione: (2026)

Preserving Conservation Laws in the Time-Evolving Natural Gradient Method via Relaxation and Projection Techniques
di: Shi, Zihao, et al.
Pubblicazione: (2026)

Generalization Error Analysis of Deep Backward Dynamic Programming for Solving Nonlinear PDEs
di: Ouyang, Du, et al.
Pubblicazione: (2024)

Approximation Theory and Applications of Randomized Neural Networks for Solving High-Dimensional PDEs
di: De Ryck, T., et al.
Pubblicazione: (2025)

Neural Evolutionary Kernel Method: A Knowledge-Guided Framework for Solving Evolutionary PDEs
di: Ling, Shuo, et al.
Pubblicazione: (2026)

What Can One Expect When Solving PDEs Using Shallow Neural Networks?
di: He, Roy Y., et al.
Pubblicazione: (2025)

Solving PDEs on Spheres with Physics-Informed Convolutional Neural Networks
di: Lei, Guanhang, et al.
Pubblicazione: (2023)

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

Incorporating Local Hölder Regularity into PINNs for Solving Elliptic PDEs
di: Zhou, Qirui, et al.
Pubblicazione: (2025)

A Quantum Spectral Framework for Solving PDEs
di: Huang, Chih-Kang, et al.
Pubblicazione: (2026)

A Least-Squares-Based Neural Network (LS-Net) for Solving Linear Parametric PDEs
di: Baharlouei, Shima, et al.
Pubblicazione: (2024)

ALM-PINNs Algorithms for Solving Nonlinear PDEs and Parameter Inversion Problems
di: Tian, Yimeng, et al.
Pubblicazione: (2024)

Predictive Moving Sample Method for Physics-Informed Neural Solvers of Time-Dependent PDEs
di: Xu, Beining, et al.
Pubblicazione: (2026)

Learn to Evolve: Self-supervised Neural JKO Operator for Wasserstein Gradient Flow
di: Feng, Xue, et al.
Pubblicazione: (2026)

Time integration of dissipative stochastic PDEs
di: Biščević, Helena, et al.
Pubblicazione: (2025)

Solving Nonlinear PDEs with Sparse Radial Basis Function Networks
di: Shao, Zihan, et al.
Pubblicazione: (2025)

Autoregression-Free Neural Operators for Time-Dependent PDEs
di: Zhang, Jiaquan, et al.
Pubblicazione: (2026)

Generalized Moving Least-Squares Methods for Solving Vector-valued PDEs on Unknown Manifolds
di: Li, Rongji, et al.
Pubblicazione: (2024)

A Meshfree RBF-FD Constant along Normal Method for Solving PDEs on Surfaces
di: Bayona, Víctor, et al.
Pubblicazione: (2024)

Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEs
di: Škardová, Kateřina, et al.
Pubblicazione: (2024)

Deep Picard Iteration for High-Dimensional Nonlinear PDEs
di: Han, Jiequn, et al.
Pubblicazione: (2024)

Antisymmetry, pseudospectral methods, and conservative PDEs
di: McLachlan, Robert, et al.
Pubblicazione: (1999)

Finite Element Eigenfunction Network (FEENet): A Hybrid Framework for Solving PDEs on Complex Geometries
di: Li, Shiyuan, et al.
Pubblicazione: (2026)

A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations
di: Liu, Shu, et al.
Pubblicazione: (2024)

Deep Ritz Physics-Informed Neural Network Method for Solving the Variational Inequality
di: Zhou, Qijia, et al.
Pubblicazione: (2026)

Neural Multiscale Decomposition for Solving The Nonlinear Klein-Gordon Equation with Time Oscillation
di: Liang, Zhangyong, et al.
Pubblicazione: (2025)

DeepONet Augmented by Randomized Neural Networks for Efficient Operator Learning in PDEs
di: Jiang, Zhaoxi, et al.
Pubblicazione: (2025)

RBF-Generated Finite Difference Method Coupled with Quadratic Programming for Solving PDEs on Surfaces with Derivative Boundary Conditions
di: Chen, Peng, et al.
Pubblicazione: (2026)

Neural Measures for learning distributions of Random PDEs
di: Arampatzis, Georgios, et al.
Pubblicazione: (2025)

Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noise
di: Hou, Yilong, et al.
Pubblicazione: (2024)

Solving elliptic PDEs in unbounded domains
di: Arjmand, Doghonay, et al.
Pubblicazione: (2024)

Adaptive Randomized Neural Networks with Locally Activation Function: Theory and Algorithm for Solving PDEs
di: Bi, Ran, et al.
Pubblicazione: (2026)

(U)NFV: Supervised and Unsupervised Neural Finite Volume Methods for Solving Hyperbolic PDEs
di: Lichtlé, Nathan, et al.
Pubblicazione: (2025)