  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.
