Deep material networks for efficient scale-bridging in thermomechanical simulations of solids
| Autor: | Gajek, Sebastian |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
deep material networks
data-driven modeling Two-scale simulations Deep Material Networks Datengetriebene Modellierung Zweiskalensimulationen micromechanics Mikromechanik machine learning Maschinelles Lernen bic Book Industry Communication::T Technology engineering agriculture::TG Mechanical engineering & materials |
| Druh dokumentu: | book |
| DOI: | 10.5445/KSP/1000155688 |
| Popis: | 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. |
| Databáze: | OAPEN Library |
| Externí odkaz: |
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