AI4Science-WestlakeU/diffphycon
[NeurIPS2024] DiffPhyCon uses generative models to control complex physical systems
DiffPhyCon helps engineers and scientists control complex physical systems, like fluid dynamics or the movement of soft robots, with greater precision and flexibility. You provide data describing the system's behavior, and it generates optimal control signals to guide the system towards a desired outcome. This is ideal for researchers and control engineers working with challenging physical simulations.
Use this if you need to find precise control sequences for highly complex or dynamic physical systems, especially when traditional control methods struggle.
Not ideal if your system is simple enough for classic control methods or if you need real-time, ultra-low-latency control in a safety-critical application without further integration.
Stars
47
Forks
4
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Nov 04, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/AI4Science-WestlakeU/diffphycon"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
ZhengYinan-AIR/Diffusion-Planner
[ICLR 2025 Oral] The official implementation of "Diffusion-Based Planning for Autonomous Driving...
intuitive-robots/MoDE_Diffusion_Policy
[ICLR 25] Code for "Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers...
caio-freitas/GraphDiffusionImitate
Diffusion-based graph generative policies for imitation learning in robotics tasks 🧠🤖
LeCAR-Lab/model-based-diffusion
Official implementation for the paper "Model-based Diffusion for Trajectory Optimization"....
Weixy21/SafeDiffuser
Safe Planning with Diffusion Probabilistic Models