| Summary: | Copyright © 2020, for this paper by its authors. Flow-induced vibration depends on a large number of parameters or features. On the one hand, the number of candidate physical features may be too big to construct an interpretable and transferrable model. On the other hand, failure to account for key dependence among features may oversimplify the model. Feature selection is found to be able to reduce the dimension of the physical problem by identifying the most important features for a certain prediction task. In this paper, a weighted sparse-input neural network (WSPINN) is proposed, where the prior physical knowledge is leveraged to constrain the neural network optimization. The effectiveness of this approach is evaluated when applied to the vortex-induced vibration of a long flexible cylinder with Reynolds number from 104 to 105. The important physical features affecting the flexible cylinders’ crossflow vibration amplitude are identified.
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