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

• Hoofdauteur: Gajek, Sebastian (auth)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: KIT Scientific Publishing 2023
• Reeks: Schriftenreihe Kontinuumsmechanik im Maschinenbau 26
• Onderwerpen: 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 toegang: OAPEN Library: download the publication; OAPEN Library: description of the publication

Samenvatting

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.

Fysieke beschrijving

1 electronic resource (326 p.)

ISBN

KSP/1000155688

Toegang

Open Access