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Focusing on deep material networks (DMN), this work establishes a mathematical foundation and introduces a novel formulation that reduces degrees of freedom. It offers an efficient solution for nonlinear DMNs, enhancing complex two-scale simulations while minimizing computational demands. Additionally, a new interpolation technique is introduced, allowing for the integration of fluctuating microstructure characteristics into macroscopic simulations, thus improving accuracy and efficiency in material modeling.
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Deep material networks for efficient scale-bridging in thermomechanical simulations of solids, Sebastian Gajek
- Langue
- Année de publication
- 2023
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- (souple)
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- Titre
- Deep material networks for efficient scale-bridging in thermomechanical simulations of solids
- Langue
- Anglais
- Auteurs
- Sebastian Gajek
- Publié
- 2023
- Format
- souple
- Pages
- 326
- ISBN13
- 9783731512783
- Séries
- Mots clés
- Nonfiction, Technologie & Ingénierie
- Description
- Focusing on deep material networks (DMN), this work establishes a mathematical foundation and introduces a novel formulation that reduces degrees of freedom. It offers an efficient solution for nonlinear DMNs, enhancing complex two-scale simulations while minimizing computational demands. Additionally, a new interpolation technique is introduced, allowing for the integration of fluctuating microstructure characteristics into macroscopic simulations, thus improving accuracy and efficiency in material modeling.