mlsanityData to 7 Oct 2026

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

arXiv 2408.03124 ↗First posted 31 Jul 2024eess.SY cs.LG cs.SYcited by 8

On the map: Diffusion models → Diffusion and flow policies for robotics

Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Diffusion and flow policies for robotics, one of 14 directions in Diffusion models. Builds on 25 of its 43 references that are in the map. First posted on arXiv 31 Jul 2024.

Builds on

25 of 43 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023Adaptive Online Replanning with Diffusion Models14 Oct
  2. 2023Diffusion Policy: Visuomotor Policy Learning via Action Diffusion7 Mar
  3. 2022Is Conditional Generative Modeling all you need for Decision-Making?28 Nov

Other references · 22

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  5. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  6. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  7. 2022Video Diffusion Modelscited by 2,280
  8. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
  9. 2022Planning with Diffusion for Flexible Behavior Synthesiscited by 1,023
  10. 2023GenCast: Diffusion-based ensemble forecasting for medium-range weathercited by 169
  11. 2020Learning to Control PDEs with Differentiable Physicscited by 131
  12. 2023AR-Diffusion: Auto-Regressive Diffusion Model for Text Generationcited by 113
10 more
  1. 2024Rolling Diffusion Modelscited by 102
  2. 2018Efficient collective swimming by harnessing vortices through deep reinforcement learningcited by 73
  3. 2021Optimal control of PDEs using physics-informed neural networkscited by 51
  4. 2023Behavior Proximal Policy Optimizationcited by 43
  5. 2021Solving PDE-constrained Control Problems Using Operator Learningcited by 41
  6. 2021Continuous-Time Model-Based Reinforcement Learningcited by 39
  7. 2024Compositional Generative Inverse Designcited by 20
  8. 2024DiffPhyCon: A Generative Approach to Control Complex Physical Systemscited by 17
  9. 2023How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learningcited by 5
  10. 2024Efficient Navigation of a Robotic Fish Swimming Across the Vortical Flow Fieldcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 5.

8 papers.

  1. 2024HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative modelscited by 2
  2. 2024Multi-modal Policies with Physics-informed Representations in Complex Fluid Environmentscited by 1
  3. 2026Accelerated Sequential Flow Matching: A Bayesian Filtering Perspectivecited by 1
  4. 2026CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projectioncited by 1
  5. 2025Sample-Efficient Diffusion-based Control of Complex Physics Systemscited by 0
  6. 2026CloDS: Visual-Only Unsupervised Cloth Dynamics Learning in Unknown Conditionscited by 0
  7. 2026MetaColloc: Optimization-Free PDE Solving via Meta-Learned Basis Functionscited by 0
  8. 2026Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospectscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Denoising Diffusion-Based Control of Nonlinear Systems
  2. 2026Diffusion Controller: Framework, Algorithms and Parameterization
  3. 2025Dynamics-aware Diffusion Models for Planning and Control
  4. 2022An optimal control perspective on diffusion-based generative modeling
  5. 2026Learning When to Stop: Prefix-Optimal Dynamic Diffusion Policies for Continuous Control
  6. 2025CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation
  7. 2026DiRecT: Safe Diffusion-Based Planning via Receding-Horizon Denoising
  8. 2026Notes on Generative Modeling for Feedback Control and Planning