A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction

• URL: http://arxiv.org/abs/2211.14680v1
• Date: Sat, 26 Nov 2022 23:14:18 GMT
• Title: A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction
• Authors: Dule Shu, Zijie Li, Amir Barati Farimani
• Abstract summary: We propose a diffusion model which only uses high-fidelity data at training. With different configurations, our model is able to reconstruct high-fidelity data from either a regular low-fidelity sample or a sparsely measured sample. Our model can produce accurate reconstruction results for 2d turbulent flows based on different input sources without retraining.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning models are gaining increasing popularity in the domain of fluid dynamics for their potential to accelerate the production of high-fidelity computational fluid dynamics data. However, many recently proposed machine learning models for high-fidelity data reconstruction require low-fidelity data for model training. Such requirement restrains the application performance of these models, since their data reconstruction accuracy would drop significantly if the low-fidelity input data used in model test has a large deviation from the training data. To overcome this restraint, we propose a diffusion model which only uses high-fidelity data at training. With different configurations, our model is able to reconstruct high-fidelity data from either a regular low-fidelity sample or a sparsely measured sample, and is also able to gain an accuracy increase by using physics-informed conditioning information from a known partial differential equation when that is available. Experimental results demonstrate that our model can produce accurate reconstruction results for 2d turbulent flows based on different input sources without retraining.

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