Workflow concepts to model nonlinear mechanics with computational intelligence

PAMM, vol. 21

Abstract

Abstract Methods from computational intelligence, such as (artificial) neural networks, have become an active research direction in mechanics, leading to the development of intelligent constitutive models, surrogate models, and meta elements. Therein, many neural network architectures are inspired by mechanical domain knowledge in the form of physics‐informed or physics‐guided neural networks. Complementary approaches that systematically analyze and compare neural networks trained on mechanical data, i.e., physics‐informing neural networks, have not yet been established. As a step in that direction, we propose a workflow concept to describe neural networks in mechanics, as well as a workflow concept to systematically search and train neural network architectures on mechanical data. The workflow concepts will be presented in the scope of Kadi4Mat. Following these workflow concepts, neural network design can be unified, compared, and interpreted, which enables explainable artificial intelligence for mechanics in future works.

Authors 5

1. Arnd Koeppe corresponding Aachen Institute of General Mechanics

   Karlsruhe Institute of Technology · RWTH Aachen University

   Affiliation as printed

   Institute of Applied Materials (IAM-CMS) Karlsruhe Institute of Technology (KIT) Straße am Forum 7 76131 Karlsruhe Germany

   Institute of General Mechanics RWTH Aachen University Eilfschornsteinstr. 18 52062 Aachen Germany

   Arnd Koeppe

   Email: [email protected]

   Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, 52062 Aachen, Germany

2. Franz Bamer Aachen Institute of General Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of General Mechanics RWTH Aachen University Eilfschornsteinstr. 18 52062 Aachen Germany

   Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, 52062 Aachen, Germany

3. Michael A. Selzer

   Karlsruhe Institute of Technology · Karlsruhe University of Applied Sciences

   Affiliation as printed

   Institute of Applied Materials (IAM-CMS) Karlsruhe Institute of Technology (KIT) Straße am Forum 7 76131 Karlsruhe Germany

   Institute of Digital Materials Science (IDM) Karlsruhe University of Applied Sciences Moltkestr. 30 76133 Karlsruhe Germany

   Institute of Digital Materials Science (IDM), Karlsruhe University of Applied Sciences, Moltkestr. 30, 76133 Karlsruhe, Germany

4. Britta Nestler

   Karlsruhe Institute of Technology · Karlsruhe University of Applied Sciences

   Affiliation as printed

   Institute of Applied Materials (IAM-CMS) Karlsruhe Institute of Technology (KIT) Straße am Forum 7 76131 Karlsruhe Germany

   Institute of Digital Materials Science (IDM) Karlsruhe University of Applied Sciences Moltkestr. 30 76133 Karlsruhe Germany

   Institute of Digital Materials Science (IDM), Karlsruhe University of Applied Sciences, Moltkestr. 30, 76133 Karlsruhe, Germany

5. Bernd A. Markert Aachen Institute of General Mechanics

   RWTH Aachen University

   Affiliation as printed

   Institute of General Mechanics RWTH Aachen University Eilfschornsteinstr. 18 52062 Aachen Germany

   Institute of General Mechanics, RWTH Aachen University, Eilfschornsteinstr. 18, 52062 Aachen, Germany