Deep material networks for efficient scale-bridging in thermomechanical simulations of solids
Overview & Synopsis
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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Document Information
| Document Title | Deep material networks for efficient scale-bridging in thermomechanical simulations of solids |
|---|---|
| Author / Organization | Gajek, Sebastian |
| Subject & Discipline | deep material networks; data-driven modeling; Two-scale simulations; Deep Material Networks; Datengetriebene Modellierung; Zweiskalensimulationen; micromechanics; Mikromechanik; machine learning; Maschinelles Lernen |
| Categories | Open Access Books |
| Publication Year | 2023 |
| License / Rights | Open Access / Creative Commons |
| Resource Verification | Listed PDF (Verification Pending) |
| Original Host Website |
Directory of Open Access Books
(www.doabooks.org)
|
| File Format | application/pdf (Portable Document Format) |
| Language | EN |
| Catalog ID | DOC-91323 |
| Original Source Link | Visit Source Page on www.doabooks.org » |