Deep Learning Approaches for Dynamic Mechanical Analysis of Viscoelastic
  Fiber Composites
Victor Hoffmann Ilias Nahmed Parisa Rastin Guénaël Cabanes Julien Boisse
The increased adoption of reinforced polymer (RP) composite materials, driven
by eco-design standards, calls for a fine balance between lightness, stiffness,
and effective vibration control. These materials are integral to enhancing
comfort, safety, and energy efficiency. Dynamic Mechanical Analysis (DMA)
characterizes viscoelastic behavior, yet there's a growing interest in using
Machine Learning (ML) to expedite the design and understanding of
microstructures. In this paper we aim to map microstructures to their
mechanical properties using deep neural networks, speeding up the process and
allowing for the generation of microstructures from desired properties.