On Flows of Neural Ordinary Differential Equations That Are Solutions of Lotka-Volterra Dynamical Systems

نویسندگان

چکیده

Neural Ordinary Differential Equations (NODE) have emerged as a novel approach to deep learning, where instead of specifying discrete sequence hidden layers, it parameterizes the derivative state using neural network [1]. The solution underlying dynamical system is flow, and various works explored universality flows, in sense being able approximate any analytical function. In this paper we present preliminary work aimed at identifying families systems ordinary differential equations (SODE) that are universal, they encompass most appear practice. Once one these (candidate) universal SODEs found, define process generates family NODEs whose flows precisely solutions found above. candidate SODE here generalized Lotka-Volterra (LV) equations. We NODE models built upon LV description their appropriate some implementations process.

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ژورنال

عنوان ژورنال: Frontiers in artificial intelligence and applications

سال: 2022

ISSN: ['1879-8314', '0922-6389']

DOI: https://doi.org/10.3233/faia220337