An Orthogonal Polynomial Kernel-Based Machine Learning Model for Differential-Algebraic Equations

• URL: http://arxiv.org/abs/2401.14382v1
• Date: Thu, 25 Jan 2024 18:37:17 GMT
• Title: An Orthogonal Polynomial Kernel-Based Machine Learning Model for Differential-Algebraic Equations
• Authors: Tayebeh Taheri, Alireza Afzal Aghaei, Kourosh Parand
• Abstract summary: We present a novel approach to solving general DAEs in an operator format by establishing connections between the LS-SVR machine learning model, weighted residual methods, and Legendres. To assess the effectiveness of our proposed method, we conduct simulations involving various DAE scenarios, such as nonlinear systems, fractional-order derivatives, integro-differential, and partial DAEs.
• Score: 0.24578723416255746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent introduction of the Least-Squares Support Vector Regression (LS-SVR) algorithm for solving differential and integral equations has sparked interest. In this study, we expand the application of this algorithm to address systems of differential-algebraic equations (DAEs). Our work presents a novel approach to solving general DAEs in an operator format by establishing connections between the LS-SVR machine learning model, weighted residual methods, and Legendre orthogonal polynomials. To assess the effectiveness of our proposed method, we conduct simulations involving various DAE scenarios, such as nonlinear systems, fractional-order derivatives, integro-differential, and partial DAEs. Finally, we carry out comparisons between our proposed method and currently established state-of-the-art approaches, demonstrating its reliability and effectiveness.

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