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Engineering Sciences

Learning a hyperelastic constitutive model from 3D experimental data

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Authors: Mathys Bourdyot, Marius Campans, Benjamin Smaniotto, Raphaël Langlois, Emmanuel Baranger, Clément Jailin

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This work was conducted at Université Paris-Saclay, CentraleSupélec, ENS Paris-Saclay, CNRS, LMPS – Laboratoire de Mécanique Paris-Saclay, Gif-sur-Yvette, France. This repository contains code and data for developing and using Physics-Augmented Neural Networks (PANN) aimed at modeling isotropic hyperelastic material behavior. It integrates TensorFlow neural network models with finite element analysis (FEA) and experimental displacement data obtained from DVC. Code and data used in: M. Bourdyot, M. Compans, R. Langlois, B. Smaniotto, E. Baranger, C. Jailin, Learning a hyperelastic constitutive model from 3D experimental data.