Numerical Solution of Stiff Ordinary Differential Equations with Random Projection Neural Networks

• URL: http://arxiv.org/abs/2108.01584v1
• Date: Tue, 3 Aug 2021 15:49:17 GMT
• Title: Numerical Solution of Stiff Ordinary Differential Equations with Random Projection Neural Networks
• Authors: Evangelos Galaris, Francesco Calabr\`o, Daniela di Serafino, Constantinos Siettos
• Abstract summary: We propose a numerical scheme based on Random Projection Neural Networks (RPNN) for the solution of Ordinary Differential Equations (ODEs) We show that our proposed scheme yields good numerical approximation accuracy without being affected by the stiffness, thus outperforming in same cases the textttode45 and textttode15s functions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a numerical scheme based on Random Projection Neural Networks (RPNN) for the solution of Ordinary Differential Equations (ODEs) with a focus on stiff problems. In particular, we use an Extreme Learning Machine, a single-hidden layer Feedforward Neural Network with Radial Basis Functions which widths are uniformly distributed random variables, while the values of the weights between the input and the hidden layer are set equal to one. The numerical solution is obtained by constructing a system of nonlinear algebraic equations, which is solved with respect to the output weights using the Gauss-Newton method. For our illustrations, we apply the proposed machine learning approach to solve two benchmark stiff problems, namely the Rober and the van der Pol ones (the latter with large values of the stiffness parameter), and we perform a comparison with well-established methods such as the adaptive Runge-Kutta method based on the Dormand-Prince pair, and a variable-step variable-order multistep solver based on numerical differentiation formulas, as implemented in the \texttt{ode45} and \texttt{ode15s} MATLAB functions, respectively. We show that our proposed scheme yields good numerical approximation accuracy without being affected by the stiffness, thus outperforming in same cases the \texttt{ode45} and \texttt{ode15s} functions. Importantly, upon training using a fixed number of collocation points, the proposed scheme approximates the solution in the whole domain in contrast to the classical time integration methods.

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