Image de couverture de livre

Chebyshev Spectral Neural Networks for Solving Partial Differential Equations

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Yin, Pengsong, Ling, Shuo, Ying, Wenjun
Format:	Preprint
Publié:	2024
Sujets:	Numerical Analysis Machine Learning Mathematical Physics
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

A Hybrid Kernel-Free Boundary Integral Method with Operator Learning for Solving Parametric Partial Differential Equations In Complex Domains
par: Ling, Shuo, et autres
Publié: (2024)

Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations
par: Kim, Chanyoung, et autres
Publié: (2026)

General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations
par: Wang, Sen, et autres
Publié: (2024)

Learning Domain-Independent Green's Function For Elliptic Partial Differential Equations
par: Negi, Pawan, et autres
Publié: (2024)

Data Generation-based Operator Learning for Solving Partial Differential Equations on Unbounded Domains
par: Wang, Jihong, et autres
Publié: (2023)

Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
par: Ma, Qinglong, et autres
Publié: (2024)

Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations
par: Zhou, Wei, et autres
Publié: (2024)

Automatic Differentiation is Essential in Training Neural Networks for Solving Differential Equations
par: Chen, Chuqi, et autres
Publié: (2024)

Solving Partial Differential Equations with Random Feature Models
par: Liao, Chunyang
Publié: (2024)

Quantum Recurrent Neural Networks with Encoder-Decoder for Time-Dependent Partial Differential Equations
par: Chen, Yuan, et autres
Publié: (2025)

Variational (Energy-Based) Spectral Learning: A Machine Learning Framework for Solving Partial Differential Equations
par: Hammad, M. M.
Publié: (2026)

Physics-embedded Fourier Neural Network for Partial Differential Equations
par: Xu, Qingsong, et autres
Publié: (2024)

Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling
par: Propp, Adrienne M., et autres
Publié: (2024)

Randomized Neural Networks for Partial Differential Equation on Static and Evolving Surfaces
par: Sun, Jingbo, et autres
Publié: (2026)

Random Neural Network Expressivity for Non-Linear Partial Differential Equations
par: Mehmood, Muhammed Ali, et autres
Publié: (2026)

Finite Expression Method for Solving High-Dimensional Partial Differential Equations
par: Liang, Senwei, et autres
Publié: (2022)

BEACONS: Bounded-Error, Algebraically-Composable Neural Solvers for Partial Differential Equations
par: Gorard, Jonathan, et autres
Publié: (2026)

A Physics-Informed Neural Network with a Modified Lorentzian Activation for Nonlocal Gradient-Flow Equations in Dynamic Density Functional Theory
par: Gourzoulidis, Dimitrios, et autres
Publié: (2026)

DeepONet for Solving Nonlinear Partial Differential Equations with Physics-Informed Training
par: Yang, Yahong
Publié: (2024)

Solving High-Dimensional Partial Integral Differential Equations: The Finite Expression Method
par: Hardwick, Gareth, et autres
Publié: (2024)

Domain-Decomposed Graph Neural Network Surrogate Modeling for Ice Sheets
par: Propp, Adrienne M., et autres
Publié: (2025)

A Data-Driven Approach to Solving First-Kind Fredholm Integral Equations and Their Convergence Analysis
par: Ling, Duan-Peng, et autres
Publié: (2025)

Weak Generative Sampler to Efficiently Sample Invariant Distribution of Stochastic Differential Equation
par: Cai, Zhiqiang, et autres
Publié: (2024)

Muti-Fidelity Prediction and Uncertainty Quantification with Laplace Neural Operators for Parametric Partial Differential Equations
par: Zheng, Haoyang, et autres
Publié: (2025)

MINPO: Memory-Informed Neural Pseudo-Operator to Resolve Nonlocal Spatiotemporal Dynamics
par: Mostajeran, Farinaz, et autres
Publié: (2025)

Physics-Informed Boundary Integral Networks (PIBI-Nets): A Data-Driven Approach for Solving Partial Differential Equations
par: Nagy-Huber, Monika, et autres
Publié: (2023)

Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations
par: Rao, Xiang, et autres
Publié: (2025)

HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
par: Bryutkin, Andrey, et autres
Publié: (2024)

Spectral methods for Neural Integral Equations
par: Zappala, Emanuele
Publié: (2023)

PICL: Physics Informed Contrastive Learning for Partial Differential Equations
par: Lorsung, Cooper, et autres
Publié: (2024)

Causal Operator Discovery in Partial Differential Equations via Counterfactual Physics-Informed Neural Networks
par: Katende, Ronald
Publié: (2025)

A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations
par: Liu, Shu, et autres
Publié: (2024)

FBSJNN: A Theoretically Interpretable and Efficiently Deep Learning method for Solving Partial Integro-Differential Equations
par: Ye, Zaijun, et autres
Publié: (2024)

A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
par: Mora, Carlos, et autres
Publié: (2024)

Quantum Implicit-Explicit Schemes for Multiscale Ordinary and Partial Differential Equations via Schrödingerization
par: Hu, Qitong, et autres
Publié: (2026)

Differentiable programming across the PDE and Machine Learning barrier
par: Bouziani, Nacime, et autres
Publié: (2024)

Newton Informed Neural Operator for Computing Multiple Solutions of Nonlinear Partials Differential Equations
par: Hao, Wenrui, et autres
Publié: (2024)

Scale-Consistent Learning for Partial Differential Equations
par: Li, Zongyi, et autres
Publié: (2025)

An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
par: Gonon, Lukas, et autres
Publié: (2024)

Generalization Limits of In-Context Operator Networks for Higher-Order Partial Differential Equations
par: Mahowald, Jamie, et autres
Publié: (2026)