  The aim of this study is to develop surrogate models for quick, accurate
prediction of thrust forces generated through flapping fin propulsion for given
operating conditions and fin geometries. Different network architectures and
configurations are explored to model the training data separately for the lead
fin and rear fin of a tandem fin setup. We progressively improve the data
representation of the input parameter space for model predictions. The models
are tested on three unseen fin geometries and the predictions validated with
computational fluid dynamics (CFD) data. Finally, the orders of magnitude gains
in computational performance of these surrogate models, compared to
experimental and CFD runs, vs their tradeoff with accuracy is discussed within
the context of this tandem fin configuration.
