A reduced model using random forest: application on car crash optimization-Reference-Cited by-同舟云学术

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A reduced model using random forest: application on car crash optimization

Published:2019-11-30 Issue:2 Volume:78 Page:193-212
ISSN:2254-3902
Container-title:SeMA Journal
language:en
Short-container-title:SeMA

Author:

Assou S.,Tourbier Y.,Gstalter E.,Charrier M.,Dessombz O.,Jézéquel L.

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Control and Optimization,Modeling and Simulation,Numerical Analysis

Link

https://link.springer.com/content/pdf/10.1007/s40324-019-00208-8.pdf

Reference20 articles.

1. Berkooz, G., Holmes, P., Lumley, J.L.: The proper orthogonal decomposition in the analysis of turbulent flows. Annu. Rev. Fluid Mech. 25(1), 539–575 (1993)

2. Bohn, B., Garcke, J., Iza-Teran, R., Paprotny, A., Peherstorfer, B., Schepsmeier, U., Thole, C.-A.: Analysis of car crash simulation data with nonlinear machine learning methods. Procedia Comput. Sci. 18, 621–630 (2013)

3. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)

4. Chatterjee, A.: An introduction to the proper orthogonal decomposition. Curr. Sci 78(7), 808–817 (2000)

5. Chen, T., Guestrin, C.: Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM sigkdd international conference on knowledge discovery and data mining, pp. 785–794. ACM, New York (2016)

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