Accelerating explicit time-stepping with spatially variable time steps through machine learning
DOI10.1007/s10915-023-02260-zOpenAlexW4380083355MaRDI QIDQ6111357
Natasha Flyer, Kiera van der Sande, Bengt Fornberg
Publication date: 6 July 2023
Published in: Journal of Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10915-023-02260-z
Finite difference methods for initial value and initial-boundary value problems involving PDEs (65M06) Spectral, collocation and related methods for initial value and initial-boundary value problems involving PDEs (65M70) Method of lines for initial value and initial-boundary value problems involving PDEs (65M20) Mesh generation, refinement, and adaptive methods for the numerical solution of initial value and initial-boundary value problems involving PDEs (65M50)
Cites Work
- Unnamed Item
- On the role of polynomials in RBF-FD approximations: II. Numerical solution of elliptic PDEs
- On the role of polynomials in RBF-FD approximations. I: Interpolation and accuracy
- Machine learning of linear differential equations using Gaussian processes
- Hidden physics models: machine learning of nonlinear partial differential equations
- Fast generation of 2-D node distributions for mesh-free PDE discretizations
- Enhanced fifth order WENO shock-capturing schemes with deep learning
- A neural network based shock detection and localization approach for discontinuous Galerkin methods
- On the role of polynomials in RBF-FD approximations. III: Behavior near domain boundaries
- Explicit time stepping of PDEs with local refinement in space-time
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Enhancing finite differences with radial basis functions: experiments on the Navier-Stokes equations
- A Primer on Radial Basis Functions with Applications to the Geosciences
- On Generation of Node Distributions for Meshless PDE Discretizations
- Fast Variable Density 3-D Node Generation
- Extremely randomized trees
This page was built for publication: Accelerating explicit time-stepping with spatially variable time steps through machine learning