Machine Learning & Computational Mechanics: the natural next step
- Referent: Prof. Dr. Christian J. Cyron
- Ort: Online (zoom)
- Termin: 11.2.2021, 14:00 - 14:45 Uhr
- Gastgeber: SFB 1173
Historically, the discipline of mechanics was dominated for most of the time by experimental methods.
It was only with the advent of the modern era around 500 years ago that theoretical methods gained
more and more importance in mechanical research. In particular partial differential equations have
become a standard tool of mechanical analysis over the last centuries. Over the last 50 years, the rapid
increase of computational power resulted in an increasing shift from theoretical analysis towards
numerical analysis, giving rise to the field of computational mechanics. At the moment we are
observing as another transformative development a rapid growth of the world-wide data sphere. It is
a key question, what effect this growth will have on our way of analyzing mechanical problems.
This talk advocates the hypothesis that it will trigger over the next decades a fusion of computational
mechanics and machine learning and thereby induce a shift of paradigm how we use partial differential
equations to describe and predict mechanical systems. We underpin this hypothesis by showing
examples of our most recent research how this fusion can be transformative both in the area of
materials research and general computational mechanics.
Authors: Christian J. Cyron(1,2), Kevin Linka(1), Roland C. Aydin(2)
1 Institute of Continuum and Materials Mechanics, Hamburg University of Technology, Hamburg
2 Institute of Material Systems Modeling, Helmholtz-Zentrum Geesthacht, Geesthacht