Buchumschlag

Data-Guided Physics-Informed Neural Networks for Solving Inverse Problems in Partial Differential Equations

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Zhou, Wei, Xu, Y. F.
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Machine Learning Numerical Analysis
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

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

Solving Forward and Inverse Problems of Contact Mechanics using Physics-Informed Neural Networks
von: Sahin, T., et al.
Veröffentlicht: (2023)

A Gaussian Process Framework for Solving Forward and Inverse Problems Involving Nonlinear Partial Differential Equations
von: Mora, Carlos, et al.
Veröffentlicht: (2024)

Physics-embedded Fourier Neural Network for Partial Differential Equations
von: Xu, Qingsong, et al.
Veröffentlicht: (2024)

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

Chebyshev Spectral Neural Networks for Solving Partial Differential Equations
von: Yin, Pengsong, et al.
Veröffentlicht: (2024)

An Overview on Machine Learning Methods for Partial Differential Equations: from Physics Informed Neural Networks to Deep Operator Learning
von: Gonon, Lukas, et al.
Veröffentlicht: (2024)

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

Solving Partial Differential Equations with Random Feature Models
von: Liao, Chunyang
Veröffentlicht: (2024)

Physics-Informed Boundary Integral Networks (PIBI-Nets): A Data-Driven Approach for Solving Partial Differential Equations
von: Nagy-Huber, Monika, et al.
Veröffentlicht: (2023)

Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations
von: Kim, Chanyoung, et al.
Veröffentlicht: (2026)

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

Solving the Wide-band Inverse Scattering Problem via Equivariant Neural Networks
von: Zhang, Borong, et al.
Veröffentlicht: (2022)

Randomized Neural Networks for Partial Differential Equation on Static and Evolving Surfaces
von: Sun, Jingbo, et al.
Veröffentlicht: (2026)

Random Neural Network Expressivity for Non-Linear Partial Differential Equations
von: Mehmood, Muhammed Ali, et al.
Veröffentlicht: (2026)

Finite Expression Method for Solving High-Dimensional Partial Differential Equations
von: Liang, Senwei, et al.
Veröffentlicht: (2022)

Deep Parallel Spectral Neural Operators for Solving Partial Differential Equations with Enhanced Low-Frequency Learning Capability
von: Ma, Qinglong, et al.
Veröffentlicht: (2024)

Examining the robustness of Physics-Informed Neural Networks to noise for Inverse Problems
von: Jekic, Aleksandra, et al.
Veröffentlicht: (2025)

PICL: Physics Informed Contrastive Learning for Partial Differential Equations
von: Lorsung, Cooper, et al.
Veröffentlicht: (2024)

Solving High-Dimensional Partial Integral Differential Equations: The Finite Expression Method
von: Hardwick, Gareth, et al.
Veröffentlicht: (2024)

Latent Neural Operator for Solving Forward and Inverse PDE Problems
von: Wang, Tian, et al.
Veröffentlicht: (2024)

A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations
von: Aghaei, Alireza Afzal
Veröffentlicht: (2024)

Quantum-Classical Physics-Informed Neural Networks for Solving Reservoir Seepage Equations
von: Rao, Xiang, et al.
Veröffentlicht: (2025)

Bayesian Physics Informed Neural Networks for Linear Inverse problems
von: Mohammad-Djafari, Ali
Veröffentlicht: (2025)

HAMLET: Graph Transformer Neural Operator for Partial Differential Equations
von: Bryutkin, Andrey, et al.
Veröffentlicht: (2024)

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

Transformed Physics-Informed Neural Networks for The Convection-Diffusion Equation
von: Guan, Jiajing, et al.
Veröffentlicht: (2024)

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

Generalization Bounds for Physics-Informed Neural Networks for the Incompressible Navier-Stokes Equations
von: Andre-Sloan, Sebastien, et al.
Veröffentlicht: (2026)

FBSJNN: A Theoretically Interpretable and Efficiently Deep Learning method for Solving Partial Integro-Differential Equations
von: Ye, Zaijun, et al.
Veröffentlicht: (2024)

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

General-Kindred Physics-Informed Neural Network to the Solutions of Singularly Perturbed Differential Equations
von: Wang, Sen, et al.
Veröffentlicht: (2024)

Solving Inverse Parametrized Problems via Finite Elements and Extreme Learning Networks
von: Burman, Erik, et al.
Veröffentlicht: (2026)

Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations
von: Li, Zhaohui, et al.
Veröffentlicht: (2022)

The Neural Network Approach to Inverse Problems in Differential Equations
von: Xu, Kailai, et al.
Veröffentlicht: (2019)

Scale-Consistent Learning for Partial Differential Equations
von: Li, Zongyi, et al.
Veröffentlicht: (2025)

Generalization Limits of In-Context Operator Networks for Higher-Order Partial Differential Equations
von: Mahowald, Jamie, et al.
Veröffentlicht: (2026)

Dual-Balancing for Physics-Informed Neural Networks
von: Zhou, Chenhong, et al.
Veröffentlicht: (2025)

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

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