Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear partial differential equations
Authors:
- Jinshuai BaiJBJinshuai Bai
- Gui-Rong LiuGLGui-Rong Liu
- Ashish GuptaAGAshish Gupta
- Laith AlzubaidiLALaith Alzubaidi
- Xi-Qiao FengXFXi-Qiao Feng
- YuanTong GuYuanTong Gu
Top Cited Papers
Open Access
- 3 August 2023
- journal article
- research article
- Published by Elsevier in Computer Methods in Applied Mechanics and Engineering
- Vol. 415
Abstract
No abstract availableKeywords
All Related Versions
Funding Information
- Australian Research Council (DP 200102546, IC190100020)
This publication has 52 references indexed in Scilit:
- Transfer learning enhanced physics informed neural network for phase-field modeling of fractureTheoretical and Applied Fracture Mechanics, 2020
- A deep energy method for finite deformation hyperelasticityEuropean Journal of Mechanics - A/Solids, 2020
- Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networksComputer Methods in Applied Mechanics and Engineering, 2020
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equationsJournal of Computational Physics, 2019
- DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamicsComputer Physics Communications, 2018
- Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum MechanicsPhysical Review Letters, 2018
- Radial point interpolation collocation method (RPICM) for partial differential equationsComputers & Mathematics with Applications, 2005
- Radial point interpolation collocation method (RPICM) for the solution of nonlinear poisson problemsComputational Mechanics, 2005
- Numerical modelling of transient viscoelastic flowsJournal of Non-Newtonian Fluid Mechanics, 2004
- On the optimal shape parameters of radial basis functions used for 2-D meshless methodsComputer Methods in Applied Mechanics and Engineering, 2002