.png){kind=icon}
.png){kind=icon}
Return
Hierarchical Differentiable Fluid Simulation
.png){kind=icon}
OA
AI
DOI：10.1111/cgf.70226.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
differentiable fluid simulation
fluid control
fluid simulation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
2.9
Papers:
497
Citations:
1.1W