Deep material networks for efficient scale-bridging in thermomechanical simulations of solids

Deep material networks for efficient scale-bridging in thermomechanical simulations of solids

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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...

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Bibliographic Details
Main Author: Gajek, Sebastian (auth)
Format: Electronic Book Chapter
Language:English
Published: KIT Scientific Publishing 2023
Series:Schriftenreihe Kontinuumsmechanik im Maschinenbau
Subjects:
Mechanical engineering & materials
deep material networks; data-driven modeling; Two-scale simulations; Deep Material Networks; Datengetriebene Modellierung; Zweiskalensimulationen; micromechanics; Mikromechanik; machine learning; Maschinelles Lernen
Online Access:DOAB: download the publication
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Summary: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.
Physical Description:1 electronic resource (326 p.)
ISBN:KSP/1000155688
Access:Open Access

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