Explore
Deep material networks for efficient scale-bridging in thermomechanical simulations of solids
Sebastian Gajek
2023
0 Ungluers have
Faved this Work
Login to Fave
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.
This book is included in DOAB.
Why read this book? Have your say.
You must be logged in to comment.
Rights Information
Are you the author or publisher of this work? If so, you can claim it as yours by registering as an Unglue.it rights holder.Downloads
This work has been downloaded 8 times via unglue.it ebook links.
- 8 - pdf (CC BY-SA) at Unglue.it.
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
Editions