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Data · dataset · 2023

Model-Driven Identification Framework for Optimal Constitutive Modelling from Kinematics and Rheological Arrangement

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Machine Learning framework for generation of constitutive models capable of automatically identifying the optimal kinematics and rheological model.

Description

It consists of a multiphysics model-driven framework that optimally selects the most suitable model kinematics, its rheological components and their arrangement for a given set of experimental curves. Subsequently, it calibrates all the material constants belonging to such a model, independent of its complexity.

In its present form, the framework can directly work with hyperelastic, viscohyperelastic and magneto-viscohyperelastic materials. Copyright (C) 2023: Ernesto Gonzalez-Saiz, Daniel Garcia-Gonzalez If using this code for research or industrial purposes, please cite: "Ernesto Gonzalez-Saiz, D. Garcia-Gonzalez. Model-Driven Identification Framework for Optimal Constitutive Modelling from Kinematics and Rheological Arrangement.

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Computer Methods in Applied Mechanics and Engineering, 2023." To use the package, import the main folder (MATSIM).

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Provenance · 1 source records, 6 field assertions
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e-cienciaDatosdoi:10.5281/ZENODO.808333813 d agoJSON v1
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