Hierarchical Differentiable Fluid Simulation

Abstract Differentiable simulation is an emerging field that offers a powerful and flexible route to fluid control. In grid‐based settings, high memory consumption is a long‐standing bottleneck that constrains optimization resolution. We introduce a two‐step algorithm that significantly reduces memory usage: our method first optimizes for bulk forces at reduced resolution, then refines local details over sub‐domains while maintaining differentiability. In trading runtime for memory, it enables optimization at previously unattainable resolutions. We validate its effectiveness and memory savings on a series of fluid control problems.