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Poster

NeuralFluid: Nueral Fluidic System Design and Control with Differentiable Simulation

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

East Exhibit Hall A-C #3700
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Fri 13 Dec 4:30 p.m. PST — 7:30 p.m. PST

Abstract:

We present NeuralFluid, 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.

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