Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models. (arXiv:2204.12584v2 [cs.RO] UPDATED)

Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of
interest to biologists and engineers. Solving the fully coupled FSI equations
for incompressible Navier-Stokes and finite elasticity is computationally
expensive. Optimizing robotic swimmer design within such a system generally
involves cumbersome, gradient-free procedures on top of the already costly
simulation. To address this challenge we present a novel, fully differentiable
hybrid approach to FSI that combines a 2D direct numerical simulation for the
deformable solid structure of the swimmer and …

arxiv dynamics network neural network optimization