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Machine learning aided multiscale mechanics of fiber suspensions
Benedikt Sterr
2025
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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.
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Keywords
- computational Micromechanics
- data-driven modelling
- Datengetriebene Modellierung
- deep material networks
- Fasersuspensionen
- Fiber suspensions
- Machine learning
- Maschinelles Lernen
- Numerische Mikromechanik
Links
DOI: 10.5445/KSP/1000179536Editions