scholarly journals Data Mining on Crash Simulation Data

2005 ◽  
pp. 558-569 ◽  
Author(s):  
Annette Kuhlmann ◽  
Ralf-Michael Vetter ◽  
Christoph Lübbing ◽  
Clemens-August Thole
Keyword(s):  
Data Mining ◽  
Simulation Data ◽  
Crash Simulation

scholarly journals A Simulation Data-Driven Design Approach for Rapid Product Optimization

2020 ◽  
Vol 20 (2) ◽  
Author(s):  
Yanli Shao ◽  
Huawei Zhu ◽  
Rui Wang ◽  
Ying Liu ◽  
Yusheng Liu
Keyword(s):  
Data Mining ◽  
Optimal Design ◽  
Prediction Model ◽  
Design Optimization ◽  
Dynamic Simulation ◽  
Design Approach ◽  
Data Driven ◽  
Design Solution ◽  
Simulation Data ◽  
Product Optimization

Abstract Traditional design optimization is an iterative process of design, simulation, and redesign, which requires extensive calculations and analysis. The designer needs to adjust and evaluate the design parameters manually and continually based on the simulation results until a satisfactory design is obtained. However, the expensive computational costs and large resource consumption of complex products hinder the wide application of simulation in industry. It is not an easy task to search the optimal design solution intelligently and efficiently. Therefore, a simulation data-driven design approach which combines dynamic simulation data mining and design optimization is proposed to achieve this purpose in this study. The dynamic simulation data mining algorithm—on-line sequential extreme learning machine with adaptive weights (WadaptiveOS-ELM)—is adopted to train the dynamic prediction model to effectively evaluate the merits of new design solutions in the optimization process. Meanwhile, the prediction model is updated incrementally by combining new “good” data set to reduce the modeling cost and improve the prediction accuracy. Furthermore, the improved heuristic optimization algorithm—adaptive and weighted center particle swarm optimization (AWCPSO)—is introduced to guide the design change direction intelligently to improve the search efficiency. In this way, the optimal design solution can be searched automatically with less actual simulation iterations and higher optimization efficiency, and thus supporting the rapid product optimization effectively. The experimental results demonstrate the feasibility and effectiveness of the proposed approach.

scholarly journals Analysis of Car Crash Simulation Data with Nonlinear Machine Learning Methods

2013 ◽  
Vol 18 ◽  
pp. 621-630 ◽  
Author(s):  
Bastian Bohn ◽  
Jochen Garcke ◽  
Rodrigo Iza-Teran ◽  
Alexander Paprotny ◽  
Benjamin Peherstorfer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Simulation Data ◽  
Learning Methods ◽  
Crash Simulation ◽  
Machine Learning Methods ◽  
Car Crash
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