Solving the Initial Value Problem of Ordinary Differential Equations by Lie Group based Neural Network Method

TL;DR

This paper introduces a novel neural network approach that integrates Lie group theory to efficiently solve initial value problems of ordinary differential equations, reducing parameters and improving accuracy.

Contribution

It combines Lie group symmetry methods with neural networks to create a more efficient and accurate solver for ODE initial value problems.

Findings

Reduces the number of trainable parameters compared to existing methods.

Achieves faster convergence and higher accuracy in learning solutions.

Successfully applied to physical oscillation problems.

Abstract

To combine a feedforward neural network (FNN) and Lie group (symmetry) theory of differential equations (DEs), an alternative artificial NN approach is proposed to solve the initial value problems (IVPs) of ordinary DEs (ODEs). Introducing the Lie group expressions of the solution, the trial solution of ODEs is split into two parts. The first part is a solution of other ODEs with initial values of original IVP. This is easily solved using the Lie group and known symbolic or numerical methods without any network parameters (weights and biases). The second part consists of an FNN with adjustable parameters. This is trained using the error back propagation method by minimizing an error (loss) function and updating the parameters. The method significantly reduces the number of the trainable parameters and can more quickly and accurately learn the real solution, compared to the existing similar methods. The numerical method is applied to several cases, including physical oscillation problems. The results have been graphically represented, and some conclusions have been made.