  Flapping-fin underwater vehicle propulsion systems provide an alternative to
propeller-driven systems in situations that require involve a constrained
environment or require high maneuverability. Testing new configurations through
experiments or high-fidelity simulations is an expensive process, slowing
development of new systems. This is especially true when introducing new fin
geometries. In this work, we propose machine learning approaches for thrust
prediction given the system's fin geometries and kinematics. We introduce
data-efficient fin shape parameterization strategies that enable our network to
predict thrust profiles for unseen fin geometries given limited fin shapes in
input data. In addition to faster development of systems, generalizable
surrogate models offer fast, accurate predictions that could be used on an
unmanned underwater vehicle control system.
