# Evaluation of Surrogate Models for Multi-fin Flapping Propulsion Systems

arXiv: https://arxiv.org/abs/1910.14194 · HTML: https://mlsanity.com/paper/1910.14194/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System](https://mlsanity.com/paper/2209.06369/) (2022)
- [Data-Driven Approaches for Thrust Prediction in Underwater Flapping Fin Propulsion Systems](https://mlsanity.com/paper/2406.01947/) (2024)

## Abstract (arXiv)

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.