MS007D Machine Learning Methods for Multiscale and Multiphysics Material Modeling IV
MS Corresponding Organizer: Prof. Fadi Aldakheel (Leibniz Universität Hannover)
Chaired by:
Prof. Fadi Aldakheel (Leibniz Universität Hannover , Germany) , Prof. Miguel Bessa (Brown University , United States)
Prof. Fadi Aldakheel (Leibniz Universität Hannover , Germany) , Prof. Miguel Bessa (Brown University , United States)
Scheduled presentations:
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Accounting for elasto-plasticity in constitutive artificial neural networks
B. Boes, J. Simon*, H. Holthusen -
Surrogate Elements for Nonlinear Microstructures using Physics-enhanced Machine Learning
W. Li*, O. Weeger -
An automated dual-stage approach for constitutive modeling of hyperelastic solids
L. Linden*, K. Kalina, J. Brummund, M. Kästner -
Physics-enhanced neural networks for hyperelastic beam modeling
J. Schommartz*, J. Alzate Cobo, D. Klein, O. Weeger -
Graph Neural Networks with Embedded Symmetries for Robust Computational Homogenization of Metamaterials
F. Hendriks*, V. Menkovski, M. Doškář, M. Geers, O. Rokoš -
A physically recurrent neural network for rate dependent composite materials
M. Maia*, I. Rocha, D. Kovačević, F. van der Meer
