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.

翻译：我们提出了一种新颖的框架，用于探索具有动态固体边界的复杂流体系统的神经控制与设计。我们的系统具有以下特点：一个处理固液界面的快速可微纳维-斯托克斯求解器、一个低维可微参数化几何表示、一个控制-形状协同设计算法，以及类似Gym的仿真环境，以促进各种流体控制设计应用。此外，我们提出了一套在高保真、高分辨率动态流体环境下的设计、控制与学习任务基准，这些任务对现有的可微流体仿真器构成了挑战。这些任务包括设计人工心脏的控制、识别机器人末端执行器的形状以及控制流体阀门。通过将我们的可微流体仿真器无缝集成到学习框架中，我们展示了在这些基准任务中成功实现的设计、控制与学习结果，其性能超越了无梯度解决方案。