Deep material networks for efficient scale-bridging in thermomechanical simulations of solids
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Beschreibung
We investigate deep material networks (DMN). We lay the mathematical foundation of DMNs and present a novel DMN formulation, which is characterized by a reduced number of degrees of freedom. We present a efficient solution technique for nonlinear DMNs to accelerate complex two-scale simulations with minimal computational effort. A new interpolation technique is presented enabling the consideration of fluctuating microstructure characteristics in macroscopic simulations.
Autor*in
Sebastian Gajek
Themen in »Deep material networks for efficient scale-bridging in thermomechanical simulations of solids«
Zweiskalensimulationen Mikromechanik Datengetriebene Modellierung Maschinelles Lernen Deep Material Networks Two-scale simulations micromechanics data-driven modeling machine learning deep material networks