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Data-driven model inclusion in physics based mechanical models.

In recent years, data driven machine learning methods for modeling mechanical system dynamics have made huge leaps. However, these methods typically require large amounts of experimental data, which is often missing for practical applications, and do not exploit prior knowledge available from physics based modeling in mechanical systems. On the other hand several researchers have made first advancements in showing how e.g. neural networks (single layer perceptrons) can be exploited for describing constitutive material behavior in mechanics, without addressing the issue of how to train these models from experimental data.In this project we aim to develop a proof of concept on the integrated exploitation of mechanical finite element models with data driven constitutive laws. In particular we will develop a first scheme where nonlinear model order reduction is exploited to enable an efficient inverse analysis of the constitutive model parameters from experimental data.
Date:29 Jan 2019 →  30 Sep 2020
Keywords:mechanical model
Disciplines:Numerical modelling and design