Rose-STL-Lab/dyffusion
[NeurIPS 2023] A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting
This tool helps scientists and engineers predict how complex systems evolve over time. Given initial measurements or conditions of a physical system, it generates a sequence of future states or snapshots. It's designed for researchers and practitioners who need accurate, physics-informed spatiotemporal forecasts.
231 stars.
Use this if you need to accurately forecast the future states of complex physical systems, like fluid dynamics or mesh deformations, based on their initial conditions.
Not ideal if you're looking for a simple, off-the-shelf solution without any code-level setup or if your data isn't structured for spatiotemporal forecasting.
Stars
231
Forks
26
Language
Python
License
Apache-2.0
Category
Last pushed
Oct 27, 2025
Commits (30d)
0
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