TY - JOUR
T1 - Analysis of car crash simulation data with nonlinear machine learning methods
AU - Bohn, Bastian
AU - Garcke, Jochen
AU - Iza-Teran, Rodrigo
AU - Paprotny, Alexander
AU - Peherstorfer, Benjamin
AU - Schepsmeier, Ulf
AU - Thole, Clemens August
N1 - Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - Nowadays, product development in the car industry heavily relies on numerical simulations. For example, it is used to explore the influence of design parameters on the weight, costs or functional properties of new car models. Car engineers spend a considerable amount of their time analyzing these influences by inspecting the arising simulations one at a time. Here, we propose using methods from machine learning to semi-automatically analyze the arising finite element data and thereby significantly assist in the overall engineering process. We combine clustering and nonlinear dimensionality reduction to show that the method is able to automatically detect parameter dependent structure instabilities or reveal bifurcations in the time-dependent behavior of beams. In particular we study recent nonlinear and sparse grid approaches, respectively. Our examples demonstrate the strong potential of our approach for reducing the data analysis effort in the engineering process, and emphasize the need for nonlinear methods for such tasks.
AB - Nowadays, product development in the car industry heavily relies on numerical simulations. For example, it is used to explore the influence of design parameters on the weight, costs or functional properties of new car models. Car engineers spend a considerable amount of their time analyzing these influences by inspecting the arising simulations one at a time. Here, we propose using methods from machine learning to semi-automatically analyze the arising finite element data and thereby significantly assist in the overall engineering process. We combine clustering and nonlinear dimensionality reduction to show that the method is able to automatically detect parameter dependent structure instabilities or reveal bifurcations in the time-dependent behavior of beams. In particular we study recent nonlinear and sparse grid approaches, respectively. Our examples demonstrate the strong potential of our approach for reducing the data analysis effort in the engineering process, and emphasize the need for nonlinear methods for such tasks.
KW - Analysis of FEM data
KW - Car crash simulation
KW - Machine learning
KW - Nonlinear dimensionality reduction
KW - Sparse grids
UR - http://www.scopus.com/inward/record.url?scp=84896994099&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84896994099&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2013.05.226
DO - 10.1016/j.procs.2013.05.226
M3 - Conference article
AN - SCOPUS:84896994099
SN - 1877-0509
VL - 18
SP - 621
EP - 630
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 13th Annual International Conference on Computational Science, ICCS 2013
Y2 - 5 June 2013 through 7 June 2013
ER -