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

TL;DR

This paper introduces a novel statistical learning tool based on PDEs that models biological mechanisms in livestock, aiming to balance precision, simplicity, and flexibility for better data-driven predictions.

Contribution

It develops a PDE-based mathematical model integrating biological determinants and a learning algorithm to extract parameters from livestock data, bridging biological realism and machine learning.

Findings

PDE model effectively captures biological dynamics.

Parameters can be learned from livestock data.

Improves predictive accuracy over traditional models.

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.