GAMM Student Chapter Bochum


Dennis M. Kochmann received his education at Ruhr-University Bochum in Germany (Dipl.-Ing. and Dr.-Ing. in Mechanical Engineering) and at the University of Wisconsin-Madison (M.Sc. in Engineering Mechanics). After postdoc positions at Wisconsin and Caltech, he joined the Aerospace Department at Caltech as Assistant Professor in 2011. In 2016 he was promoted to Professor of Aerospace, a position he held through 2019. Since April 2017 he has been Professor of Mechanics and Materials at ETH Zürich, where he served as Head of the Institute of Mechanical Systems and is currently Deputy Head of the Department of Mechanical and Process Engineering. His research focuses on the link between microstructure and properties of a variety of (natural and architected) materials, which includes the development of theoretical, computational and experimental methods to bridge across scales from nano to macro – tackling both forward and inverse homogenization problems. He was a Fulbright and Feodor-Lynen fellow, and his research has been recognized by, among others, the Bureau Prize in Solid Mechanics form IUTAM, the Richard von Mises Prize by GAMM, an NSF CAREER Award, ASME’s T.J.R. Hughes Young Investigator Award, an ERC Consolidator Grant, and the John Argyris Award from the IACM.

Talk Content

Architected materials (or mechanical metamaterials) with well designed macroscale properties and performance based on a careful design of the microscale architecture have gained popularity for applications ranging from wave guides and cloaks to patient-specific implants to mechanical logic and ultralow-weight structural materials. While the forward homogenization challenge (i.e., the computation of effective material properties for a given microscale architecture) is well established with numerous modeling techniques available, the inverse homogenization challenge (i.e., the identification of microscale architectures that yield specific target properties on the macroscale) is still an open challenge for many properties and metamaterial designs. We here discuss new strategies based on machine learning to tackle this inverse problem, which can be applied equally to periodic truss architectures and non-periodic spinodoid designs, for which we highlight opportunities and applications

Imprint | CC BY-SA 4.0 GAMM Student Chapter Bochum.

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