This repository provides Python code for various numerical techniques to solve Ordinary Differential Equations (ODEs). The implemented methods include the Euler method, Symplectic Euler method, ...
Efficient Solution of Nonlinear Ordinary Differential Equations Expressed in S-System Canonical Form
SIAM Journal on Numerical Analysis, Vol. 27, No. 3 (Jun., 1990), pp. 704-735 (32 pages) Ordinary differential equations can be recast into a nonlinear canonical form called an S-system. Evidence for ...
Ordinary differential equations (ODEs) and difference equations serve as complementary tools in the mathematical modelling of processes evolving in continuous and discrete time respectively. Together ...
Mathematics of Computation, Vol. 33, No. 148 (Oct., 1979), pp. 1251-1256 (6 pages) A polynomial representation of the hybrid methods for solving ordinary differential equations is presented. The ...
This repository contains an in-depth tutorial to help AI/ML practitioners successfully use neural ordinary differential equations (Neural ODEs or NODEs), understand the mathematics, know which types ...
The purpose of this research is to investigate the effciency of explicit diagonally implicit multi-stage integration methods with extrapolation. The author gave detailed explanation of explicit ...
Abstract: Physics-Informed Neural Networks (PINNs) have recently emerged as a powerful method for solving differential equations by leveraging machine learning techniques. However, while neural ...
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