Machine learning aided multiscale mechanics of fiber suspensions

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 lever...

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Bibliografiska uppgifter

Huvudupphov:	Sterr, Benedikt
Materialtyp:	Online
Språk:	engelska
Utgiven:	KIT Scientific Publishing 2025
Ämnen:	Maschinelles Lernen Datengetriebene Modellierung Numerische Mikromechanik Fasersuspensionen Deep Material Networks Machine learning Data-driven modelling computational Micromechanics Fiber suspensions thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGB Mechanical engineering
Länkar:	ONIX_20251202T160246_9783731514213_32
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