Toward mechanistic models “augmented” by machine learning. Example of a drying simulation data set exploited by a Physics Informed Neural Network
Year: 2024
Type: article
Abstract: Mechanistic modeling of drying is well-established since several decades. Based on fundamental balance equations and driven by relevant material parameters, it can predict the entire process, including configurations that were not observed before. Besides, thanks to their ability to tackle non-linear and dynamics problems, approaches based on Machine Learning (ML) based are capable of coping with complex situations even better than mechanistic modeling. The main drawback of mechanistic models is their complexity as operational tools, namely in providing the whole set of product characteristics, while the main drawback of ML tools is its restriction to the domain paved by the data set. This paper summarizes the physics of the mechanistic formulation and then presents the different possibilities of coupling the mechanistic and ML approaches to obtain an “augmented” mechanistic model. The idea is to merge the advantages of both worlds. Different strategies can be imagined: A full coupled method (hybrid model)A fully decoupled methodA cascade coupling The second part of the paper gives examples of a fully decoupled approach the mechanistic model is used to populate a data set, which is further exploited by a neural network. The originality of the work is to compare a classical neural network with a Physics Informed Neural Network.
Source: Drying Technology
Author: Patrick Perré
Language: English
Topic: Food Drying and Modeling
Subfield: Food Science
Domain: Life Sciences
Open Access status: green
Award: G7799207878
OpenAlex ID: W4403365139