tum-pbs/Diffusion-based-Flow-Prediction
Official implementation of the AIAA Journal paper "Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models"
This project helps aerospace engineers and fluid dynamicists predict how air flows around aircraft wings, even when simulation inputs have uncertainties. You provide simulation parameters like wing shape or angle of attack, and it generates a range of possible flow field solutions, highlighting the likely variations. This allows designers to understand the inherent uncertainty in their simulations.
No commits in the last 6 months.
Use this if you need to understand the full range of possible outcomes and the inherent uncertainties in your airfoil flow simulations, rather than just a single deterministic prediction.
Not ideal if you only need a single, deterministic prediction of flow fields without considering the variability or uncertainty in the results.
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85
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7
Language
Jupyter Notebook
License
Apache-2.0
Category
Last pushed
Nov 04, 2024
Commits (30d)
0
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