Neural Fluidic System Design and Control with Differentiable Simulation
<|reference_start|>Neural Fluidic System Design and Control with Differentiable Simulation: 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.<|reference_end|>
@article{li2024neuralfluid:,
    title={NeuralFluid: Neural Fluidic System Design and Control with
  Differentiable Simulation},
    author={Yifei Li, Yuchen Sun, Pingchuan Ma, Eftychios Sifakis, Tao Du, Bo Zhu,
  Wojciech Matusik},
    journal={arXiv preprint arXiv:2405.14903},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14903},
    primaryClass={physics.flu-dyn cs.AI cs.GR}
}