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Deep material networks for efficient scale-bridging in thermomechanical simulations of solids
Sebastian Gajek
2023
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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 minimal computational effort. A new interpolation technique is presented enabling the consideration of fluctuating microstructure characteristics in macroscopic simulations.
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Keywords
- data-driven modeling
- Datengetriebene Modellierung
- deep material networks
- Industrial Chemistry
- Industrial chemistry & manufacturing technologies
- Machine learning
- Maschinelles Lernen
- micromechanics
- Mikromechanik
- Pharmaceutical technology
- Technology, engineering, agriculture
- Two-scale simulations
- Zweiskalensimulationen
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