Numerical error estimation with physics informed neural network

Adhika Satyadharma

doi:10.1016/j.compfluid.2025.106700

2025-06-09

PRE

DOI：10.1016/j.compfluid.2025.106700

原文链接

原文求助

摘要

En 中文

• 开发一个利用PINN来估计模拟数据数值误差的框架。 • 证明该估计方法能够准确且可靠地工作。 • 该估计方法适用于单个模拟数据集。 • 该估计方法适用于细网格、粗网格，甚至适用于非渐近范围的情况。

期刊

Computers and Fluids

IF:

论文数:

333

被引数:

1.4W

机构

暂无机构信息

引用论文

Evaluation of numerical error estimation based on grid refinement studies with the method of the manufactured solutions

Computers & Fluids

IF0

2009-09-01

PREAI

L. Eça; M. Hoekstra

Richardson Extrapolation-Based Discretization Uncertainty Estimation for Computational Fluid Dynamics

Journal of Fluids Engineering

IF0

2014-09-10

PREAI

Tyrone S. Phillips; Christopher J. Roy

Maximum-likelihood estimators in physics-informed neural networks for high-dimensional inverse problems

COMPUTERS & CHEMICAL ENGINEERING

IF3.9

2024-02-01

OAAI

Gusmao, Gabriel S.; Medford, Andrew J.

Verification of Finite Volume Computations on Steady-State Fluid Flow and Heat Transfer

Journal of Fluids Engineering

IF0

2001-11-12

PREAI

J. Cadafalch; C. D. Pe´rez-Segarra; R. Co`nsul; A. Oliva

Algorithm 778: L-BFGS-B 算法778: l-bfgs-b

ACM Transactions on Mathematical Software

IF0

1997-12-01

OAAI

Ciyou Zhu; Richard H. Byrd; Peihuang Lu; Jorge Nocedal

Development and Evaluation of an a Posteriori Method for Estimating and Correcting Grid-Induced Errors in Solutions of the Navier-Stokes Equations

47th AIAA Aerospace Sciences Meeting including The New Horizons Forum and Aerospace Exposition

IF0

2009-06-15

PREAI

Tom Shih; Brandon Williams

Limitations of Richardson Extrapolation and Some Possible Remedies

Journal of Fluids Engineering

IF0

2005-04-28

PREAI

Ismail Celik; Jun Li; Gusheng Hu; Christian Shaffer

LIMITATIONS OF PHYSICS INFORMED MACHINE LEARNING FOR NONLINEAR TWO-PHASE TRANSPORT IN POROUS MEDIA

Journal of Machine Learning for Modeling and Computing

IF0

2020-01-01

OAAI

Olga Fuks; Hamdi A. Tchelepi

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations 物理信息神经网络: 一种用于解决涉及非线性偏微分方程的正反问题的深度学习框架

JOURNAL OF COMPUTATIONAL PHYSICS

IF3.8

2019-02-01

8.2K

OAAI

Raissi, M.; Perdikaris, P.; Karniadakis, G. E.

Physics-informed neural networks for inverse problems in supersonic flows 用于超音速流中逆问题的物理通知神经网络

JOURNAL OF COMPUTATIONAL PHYSICS

IF3.8

2022-10-01

152

OAAI

Jagtap, Ameya D.; Mao, Zhiping; Adams, Nikolaus; Karniadakis, George Em

查看更多内容