Physics-informed neural networks with parameter asymptotic strategy for learning singularly perturbed convection-dominated problem
出版年份 2023 全文链接
标题
Physics-informed neural networks with parameter asymptotic strategy for learning singularly perturbed convection-dominated problem
作者
关键词
-
出版物
COMPUTERS & MATHEMATICS WITH APPLICATIONS
Volume 150, Issue -, Pages 229-242
出版商
Elsevier BV
发表日期
2023-10-03
DOI
10.1016/j.camwa.2023.09.030
参考文献
相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。- Numerical treatment of a singularly perturbed 2-D convection-diffusion elliptic problem with Robin-type boundary conditions
- (2023) Ram Shiromani et al. APPLIED NUMERICAL MATHEMATICS
- Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre–Green–Naghdi equations
- (2022) Ameya D. Jagtap et al. OCEAN ENGINEERING
- A-PINN: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations
- (2022) Lei Yuan et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Physics-informed neural networks for inverse problems in supersonic flows
- (2022) Ameya D. Jagtap et al. JOURNAL OF COMPUTATIONAL PHYSICS
- When Do Extended Physics-Informed Neural Networks (XPINNs) Improve Generalization?
- (2022) Zheyuan Hu et al. SIAM JOURNAL ON SCIENTIFIC COMPUTING
- Error analysis for physics-informed neural networks (PINNs) approximating Kolmogorov PDEs
- (2022) Tim De Ryck et al. ADVANCES IN COMPUTATIONAL MATHEMATICS
- A finite difference method for a singularly perturbed 2‐D elliptic convection‐diffusion PDEs on Shishkin‐type meshes with non‐smooth convection and source terms
- (2022) Ram Shiromani et al. MATHEMATICAL METHODS IN THE APPLIED SCIENCES
- A computational method for a two-parameter singularly perturbed elliptic problem with boundary and interior layers
- (2022) Ram Shiromani et al. MATHEMATICS AND COMPUTERS IN SIMULATION
- High-order finite element method on a Bakhvalov-type mesh for a singularly perturbed convection–diffusion problem with two parameters
- (2021) Jin Zhang et al. APPLIED MATHEMATICS AND COMPUTATION
- Convergence analysis of weak Galerkin flux-based mixed finite element method for solving singularly perturbed convection-diffusion-reaction problem
- (2021) Zeinab Gharibi et al. APPLIED NUMERICAL MATHEMATICS
- Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs
- (2021) Siddhartha Mishra et al. IMA JOURNAL OF NUMERICAL ANALYSIS
- Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics
- (2021) Weiqi Ji et al. JOURNAL OF PHYSICAL CHEMISTRY A
- A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems
- (2021) Khemraj Shukla et al. IEEE SIGNAL PROCESSING MAGAZINE
- When and why PINNs fail to train: A neural tangent kernel perspective
- (2021) Sifan Wang et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Parallel physics-informed neural networks via domain decomposition
- (2021) Khemraj Shukla et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions
- (2021) Ameya D. Jagtap et al. NEUROCOMPUTING
- Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
- (2020) Ameya D. Jagtap et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks
- (2020) Ameya D. Jagtap et al. PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
- A parameter-uniform hybrid finite difference scheme for singularly perturbed system of parabolic convection-diffusion problems
- (2019) Maneesh Kumar Singh et al. INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS
- Exponential compact ADI method for a coupled system of convection-diffusion equations arising from the 2D unsteady magnetohydrodynamic (MHD) flows
- (2019) S. Wu et al. APPLIED NUMERICAL MATHEMATICS
- Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
- (2019) Ameya D. Jagtap et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Physics-informed neural networks for high-speed flows
- (2019) Zhiping Mao et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- An Adaptive Staggered Discontinuous Galerkin Method for the Steady State Convection–Diffusion Equation
- (2018) Jie Du et al. JOURNAL OF SCIENTIFIC COMPUTING
- A Weak Galerkin Finite Element Method for Singularly Perturbed Convection-Diffusion--Reaction Problems
- (2018) Runchang Lin et al. SIAM JOURNAL ON NUMERICAL ANALYSIS
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- (2018) M. Raissi et al. JOURNAL OF COMPUTATIONAL PHYSICS
- A robust finite difference scheme for strongly coupled systems of singularly perturbed convection-diffusion equations
- (2017) Po-Wen Hsieh et al. NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS
- An adaptive mesh strategy for singularly perturbed convection diffusion problems
- (2015) Vivek Kumar et al. APPLIED MATHEMATICAL MODELLING
- SDFEM with non-standard higher-order finite elements for a convection-diffusion problem with characteristic boundary layers
- (2011) Sebastian Franz BIT NUMERICAL MATHEMATICS
- Multigrid method based on the transformation-free HOC scheme on nonuniform grids for 2D convection diffusion problems
- (2011) Yongbin Ge et al. JOURNAL OF COMPUTATIONAL PHYSICS
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started