Using support vector machines for the computationally efficient identification of acceptable design parameters in computer-aided engineering applications

作者：

Highlights：

• SVMs are used to estimate the boundary of acceptable design parameters.

• An active learning method is developed to efficiently refine the boundary estimate.

• The algorithm is applied to a (known) toy function to demonstrate its effectiveness.

• The approach is subsequently used to find the dynamic stability limit of a train.

摘要

•SVMs are used to estimate the boundary of acceptable design parameters.•An active learning method is developed to efficiently refine the boundary estimate.•The algorithm is applied to a (known) toy function to demonstrate its effectiveness.•The approach is subsequently used to find the dynamic stability limit of a train.

论文关键词：Support vector machines,Zero finding,Design criteria evaluation,Train dynamics,Active learning

论文评审过程：Received 25 July 2016, Revised 15 February 2017, Accepted 23 March 2017, Available online 23 March 2017, Version of Record 30 March 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.03.050