NeuralFluid: Nueral Fluidic System Design and Control with Differentiable Simulation

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.

@misc{li2024neuralfluidicdesigncontrol,
  title={Neural Fluidic System Design and Control with Differentiable Simulation}, 
  author={Yifei Li and Yuchen Sun and Pingchuan Ma and Eftychios Sifakis and Tao Du and Bo Zhu and Wojciech Matusik},
  year={2024},
  eprint={2405.14903},
  archivePrefix={arXiv},
  primaryClass={physics.flu-dyn},
  url={https://arxiv.org/abs/2405.14903},