Automated differential equation solver based on the parametric approximation optimization
Not on the map. Why?
Abstract (arXiv)
The numerical methods for differential equation solution allow obtaining a discrete field that converges towards the solution if the method is applied to the correct problem. Nevertheless, the numerical methods have the restricted class of the equations, on which the convergence with a given parameter set or range is proved. Only a few "cheap and dirty" numerical methods converge on a wide class of equations without parameter tuning with the lower approximation order price. The article presents a method that uses an optimization algorithm to obtain a solution using the parameterized approximation. The result may not be as precise as an expert one. However, it allows solving the wide class of equations in an automated manner without the algorithm's parameters change.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 33 references that are in the map. First posted on arXiv 11 May 2022.
Builds on
11 of 33 references.
References · 11
- 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
- 2020Neural Operator: Graph Kernel Network for Partial Differential Equationscited by 734
- 2017DGM: A deep learning algorithm for solving partial differential equationscited by 678
- 2020When and why PINNs fail to train: A neural tangent kernel perspectivecited by 634
- 2019DeepXDE: A deep learning library for solving differential equationscited by 619
- 2020Universal Differential Equations for Scientific Machine Learningcited by 353
- 2020On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networkscited by 284
- 2018PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Networkcited by 210
- 2017Sobolev Training for Neural Networkscited by 143
- 2019DiffEqFlux.jl - A Julia Library for Neural Differential Equationscited by 33
- 2018Confederated Modular Differential Equation APIs for Accelerated Algorithm Development and Benchmarkingcited by 3
Built on by
Citing papers per year: 2023: 1.
1 paper.
- 2023Towards true discovery of the differential equationscited by 0
- 2023Towards true discovery of the differential equations9 Aug 2023