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An innovating Statistical Learning Tool based on Partial Differential Equations, intending livestock Data Assimilation

Abstract : The realistic modeling intended to quantify precisely some biological mechanisms is a task requiering a lot of a priori knowledge and generally leading to heavy mathematical models. On the other hand, the structure of the classical Machine Learning algorithms, such as Neural Networks, limits their flexibility and the possibility to take into account the existence of complex underlying phenomena, such as delay, saturation and accumulation. The aim of this paper is to reach a compromise between precision, parsimony and flexibility to design an efficient biomimetic predictive tool extracting knowledge from livestock data. To achieve this, we build a Mathematical Model based on Partial Differential Equations (PDE) embarking the mathematical expression of biological determinants. We made the hypothesis that all the physico-chemical phenomena occurring in animal body can be summarized by the evolution of a global information. Therefore the developed PDE system describes the evolution and the action of an information circulating in an Avatar of the Real Animal. This Avatar outlines the dynamics of the biological reactions of animal body in the framework of a specific problem. Each PDE contains parameters corresponding to biological-like factors which can be learnt from data by the developed Statistical Learning Tool.
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Preprints, Working Papers, ...
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Contributor : Hélène Flourent Connect in order to contact the contributor
Submitted on : Saturday, October 26, 2019 - 4:53:47 PM
Last modification on : Monday, October 11, 2021 - 2:23:10 PM
Long-term archiving on: : Monday, January 27, 2020 - 2:12:55 PM


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  • HAL Id : hal-02079750, version 2
  • ARXIV : 1903.12423



Hélène Flourent, Emmanuel Frénod, Vincent Sincholle. An innovating Statistical Learning Tool based on Partial Differential Equations, intending livestock Data Assimilation. 2019. ⟨hal-02079750v2⟩



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