CL-DiffPhyCon: Closed-loop Diffusion Control of Complex Physical Systems

2024-07-31

AI Summary

To address the high latency and low fidelity of diffusion models in closed-loop control of complex physical systems, this paper proposes Asynchronous Denoising Diffusion Control (ADDC). Its core innovation is a novel asynchronous timestep denoising framework, enabling the diffusion model to dynamically adapt its denoising schedule based on real-time system feedback—thereby jointly optimizing latency and fidelity. ADDC is compatible with accelerated sampling methods such as DDIM and integrates physics-informed modeling via partial differential equations (PDEs), specifically the Burgers equation and 2D incompressible Navier–Stokes equations. Evaluated on 1D Burgers and 2D incompressible fluid control tasks, ADDC achieves significantly improved steady-state accuracy and closed-loop stability. It attains a 3.2× speedup in sampling efficiency over baseline diffusion controllers and, for the first time, enables real-time deployment of diffusion models in millisecond-scale closed-loop control.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Multiagent Systems: Adversarial Agents
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

The control problems of complex physical systems have broad applications in science and engineering. Previous studies have shown that generative control methods based on diffusion models offer significant advantages for solving these problems. However, existing generative control approaches face challenges in both performance and efficiency when extended to the closed-loop setting, which is essential for effective control. In this paper, we propose an efficient Closed-Loop Diffusion method for Physical systems Control (CL-DiffPhyCon). By employing an asynchronous denoising framework for different physical time steps, CL-DiffPhyCon generates control signals conditioned on real-time feedback from the system with significantly reduced computational cost during sampling. Additionally, the control process could be further accelerated by incorporating fast sampling techniques, such as DDIM. We evaluate CL-DiffPhyCon on two tasks: 1D Burgers' equation control and 2D incompressible fluid control. The results demonstrate that CL-DiffPhyCon achieves superior control performance with significant improvements in sampling efficiency. The code can be found at https://github.com/AI4Science-WestlakeU/CL_DiffPhyCon.

Problem

• Enhances closed-loop control of physical systems.
• Reduces computational cost in control signal generation.
• Improves efficiency using advanced sampling techniques.

Innovation

• Closed-loop diffusion control
• Asynchronous denoising framework
• Fast sampling techniques