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Prediction of Multi-Physical Analysis Using Machine Learning
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  • Journal title : Journal of IKEEE
  • Volume 20, Issue 1,  2016, pp.94-102
  • Publisher : Institude of Korean Electrical and Electronics Engineers
  • DOI : 10.7471/ikeee.2016.20.1.094
 Title & Authors
Prediction of Multi-Physical Analysis Using Machine Learning
Lee, Keun-Myoung; Kim, Kee-Young; Oh, Ung; Yoo, Sung-kyu; Song, Byeong-Suk;
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This paper proposes a new prediction method to reduce times and labor of repetitive multi-physics simulation. To achieve exact results from the whole simulation processes, complex modeling and huge amounts of time are required. Current multi-physics analysis focuses on the simulation method itself and the simulation environment to reduce times and labor. However this paper proposes an alternative way to reduce simulation times and labor by exploiting machine learning algorithm trained with data set from simulation results. Through comparing each machine learning algorithm, Gaussian Process Regression showed the best performance with under 100 training data and how similar results can be achieved through machine-learning without a complex simulation process. Given trained machine learning algorithm, it's possible to predict the result after changing some features of the simulation model just in a few second. This new method will be helpful to effectively reduce simulation times and labor because it can predict the results before more simulation.
Multi-physics;Machine Learning;Multi-layer perceptron;Gaussian Process;M5P Model Tree;
 Cited by
Youngtaek Im, "Multi-physics analysis case using ANSYS software", Journal of KSME, Vol. 54, No. 6, p46, June 2014


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