Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models

• URL: http://arxiv.org/abs/2204.12584v1
• Date: Wed, 30 Mar 2022 15:21:44 GMT
• Title: Fast Aquatic Swimmer Optimization with Differentiable Projective Dynamics and Neural Network Hydrodynamic Models
• Authors: Elvis Nava, John Zhang, Mike Yan Michelis, Tao Du, Pingchuan Ma, Benjamin Grewe, Wojciech Matusik, Robert Katzschmann
• Abstract summary: Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of interest to biologists and engineers. We present a novel, fully differentiable hybrid approach to FSI that combines a 2D numerical simulation for the deformable solid structure of the swimmer. We demonstrate the computational efficiency and differentiability of our hybrid simulator on a 2D carangiform swimmer.
• Score: 23.480913364381664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 a physics-constrained neural network surrogate to capture hydrodynamic effects of the fluid. For the deformable simulation of the swimmer's body, we use state-of-the-art techniques from the field of computer graphics to speed up the finite-element method (FEM). For the fluid simulation, we use a U-Net architecture trained with a physics-based loss function to predict the flow field at each time step. The pressure and velocity field outputs from the neural network are sampled around the boundary of our swimmer using an immersed boundary method (IBM) to compute its swimming motion accurately and efficiently. We demonstrate the computational efficiency and differentiability of our hybrid simulator on a 2D carangiform swimmer. Since both the solid simulator and the hydrodynamics model are automatically differentiable, we obtain a fully differentiable FSI simulator that can be used for computational co-design of geometry and controls for rigid and soft bodies immersed in fluids, such as minimizing drag, maximizing speed, or maximizing efficiency via direct gradient-based optimization.

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