Deep material networks for efficient scale-bridging in thermomechanical simulations of solids
(Sprache: Englisch)
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...
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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.
Bibliographische Angaben
- Autor: Sebastian Gajek
- 2023, 326 Seiten, mit Abbildungen, Masse: 14,8 x 21 cm, Kartoniert (TB), Englisch
- Verlag: KIT Scientific Publishing
- ISBN-10: 3731512785
- ISBN-13: 9783731512783
Sprache:
Englisch
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