Data-driven Surrogate Modeling of Structural Dynamical Systems

Abstract

High-fidelity simulation models provide insight into complex dynamical systems and allow to predict their behavior under various conditions. Unfortunately, they are in general neither suitable for time-critical applications nor for evaluation on resourcelimited hardware. This results in a high demand for efficient surrogate models that require less computational effort while retaining the most important aspects of the original model. Surrogate modeling of structural dynamics systems such as finite element simulation models faces significant challenges. Some of the reasons are (i) the sheer dimensionality of such systems, (ii) the inaccessible code of commercial software, and (iii) the computationally intensive data generation. Data-based non-intrusive model order reduction hasemerged as a potent solution to the task of creating efficient yet accurate surrogate models for these kind of systems, addressing two main problems. One is the identification of coordinates that are simultaneously low-dimensional to ensure computational efficiency and yet adequately describe the system and its dynamics. The other is the approximation of the (parameter-dependent) system behavior in the identified reduced coordinates. In this talk we will present different methods to tackle both problems and discuss their advantages and disadvantages in different situations including dynamic simulations of human body models and simplified crash scenarios.

Jonas Kneifl

PhD Student in Simulation Technology

My research interests combines model order reduction, surrogate modeling and machine learning.