Fluid Control with Localized Spacetime Windows

2025-11-19

AI Summary

Addressing the high computational cost of global optimization and difficulties in enforcing boundary consistency in large-scale particle-based free-surface fluid control, this paper proposes a physics-driven control method based on local spatiotemporal windows. The method operates by (1) dynamically optimizing the temporal window size within user-specified spatial regions, using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) to adaptively search for optimal window parameters; and (2) introducing a floating background grid to apply control forces—thereby decoupling control dimensions from simulation dimensions and eliminating the need for explicit boundary condition modeling. This approach significantly reduces optimization complexity while enabling efficient and precise fluid behavior control in both 2D and 3D complex scenes. Experimental results demonstrate substantial improvements in feasibility and real-time performance for large-scale fluid control tasks.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Computer Vision: Low Level & Physics-based Vision
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

We present a physics-based fluid control method utilizing localized spacetime windows, extending force-based spacetime control to simulation scales that were previously intractable. Building on the observation that optimal control force distributions are often localized, we show that operating only in a localized spacetime window around the edit of interest can improve performance. To determine the optimal spacetime window size, we employ the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) method to search for the optimal temporal window size within a user-defined spatial region. Instead of using a Lagrangian representation, we optimize and apply control forces on a"floating"background grid, decoupling the control dimensionality from the simulation and enabling seamless integration with particle-based methods. Moreover, since the boundary conditions of the localized areas are encoded in the objective function, no extra effort is required to ensure consistency between the local control region and the global simulation domain. We demonstrate the effectiveness and efficiency of our method with various 2D and 3D particle-based free-surface simulation examples.

Problem

• Extending fluid control to previously intractable simulation scales
• Optimizing localized spacetime windows around areas of interest
• Enabling seamless control integration with particle-based fluid methods

Innovation

• Localized spacetime windows improve fluid control performance
• CMA-ES optimizes temporal window size in spatial regions
• Floating background grid decouples control from simulation