Paper

NeuralFluid: Neural Fluidic System Design and Control with Differentiable Simulation

Sun, Yuchen, Matusik, Wojciech, Zhu, Bo, Du, Tao, Li, Yifei, Sifakis, Eftychios, Ma, Pingchuan

2024

Overview

We present a novel framework to explore neural control and design of complex fluidic systems with dynamic solid boundaries. Our system features a fast differentiable Navier-Stokes solver with solid-fluid interface handling, a low-dimensional differentiable parametric geometry representation, a control-shape co-design algorithm, and gym-like simulation environments to facilitate various fluidic control design applications. Additionally, we present a benchmark of design, control, and learning tasks on high-fidelity, high-resolution dynamic fluid environments that pose challenges for existing differentiable fluid simulators. These tasks include designing the control of artificial hearts, identifying robotic end-effector shapes, and controlling a fluid gate. By seamlessly incorporating our differentiable fluid simulator into a learning framework, we demonstrate successful design, control, and learning results that surpass gradient-free solutions in these benchmark tasks.

• Publisher: Cornell University Library, arXiv.org
• Subject: Algorithms / Artificial organs / Benchmarks / Co-design / Cognitive tasks / Control systems / End effectors / Learning / Robot control / Simulators / Systems design