Machine learning aided multiscale mechanics of fiber suspensions
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Beschreibung
We present a Fast-Fourier-Transform (FFT) based computational approach to computing the viscous stress response of rigid fibers suspended in a non-Newtonian medium. We identify closed-form models for the fiber suspension viscosity from data obtained with the FFT-based computational approach by leveraging supervised machine learning techniques. Furthermore, we present a novel Deep Material Network architecture capable of treating suspensions of rigid particles with high computational efficiency.
Autor*in
Benedikt Sterr
Themen in »Machine learning aided multiscale mechanics of fiber suspensions«
Maschinelles Lernen Datengetriebene Modellierung Numerische Mikromechanik, Fasersuspensionen Deep Material Networks Machine learning Data-driven modelling computational Micromechanics Fiber suspensions