Data-driven fractional algebraic system solver

Emmanuel Lorin and Howl Nhan

Mathematics and Computers in Simulation (MATCOM), 2025, vol. 236, issue C, 170-182

Abstract: In this paper, we explore a class of (data-driven/supervised) neural network-based algorithms for solving linear and fractional algebraic systems. The latter are reformulated as dynamical systems, and solved using neural networks. Some mathematical proprieties of the derived algorithms are proposed, as well as several illustrating numerical experiments.

Keywords: Fractional linear system; Neural networks; Scientific machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:236:y:2025:i:c:p:170-182

DOI: 10.1016/j.matcom.2025.03.004

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