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Deep material networks for efficient scale-bridging in thermomechanical simulations of solids
Abstract
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- doc-type:doctoralThesis
- Text
- info:eu-repo/semantics/doctoralThesis
- dissertation
- info:eu-repo/semantics/publishedVersion
- Zweiskalensimulationen
- Mikromechanik
- Datengetriebene Modellierung
- Maschinelles Lernen
- Deep Material Networks
- Two-scale simulations
- micromechanics
- data-driven modeling
- machine learning
- deep material networks
- ddc:620
- Engineering & allied operations
- info:eu-repo/classification/ddc/620
Similar works
This paper was published in KITopen.
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