Concerning the Use of Turbulent Flow Data for Machine Learning
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866929620426686464 |
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| author | Sardar, Mohammed Zimoń, Małgorzata J. Draycott, Samuel Revell, Alistair Skillen, Alex |
| author_facet | Sardar, Mohammed Zimoń, Małgorzata J. Draycott, Samuel Revell, Alistair Skillen, Alex |
| contents | This article describes some common issues encountered in the use of Direct Numerical Simulation (DNS) turbulent flow data for machine learning. We focus on two specific issues; 1) the requirements for a fair validation set, and 2) the pitfalls in downsampling DNS data before training. We attempt to shed light on the impact these issues can have on machine learning and computer vision for turbulence. Further, we include statistical and spectral analysis for the homogenous isotropic turbulence from the John Hopkins Turbulence Database, a Kolmogorov flow, and a Rayleigh-Bénard Convection Cell using data generated by the authors, to concretely demonstrate these issues. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_06050 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Concerning the Use of Turbulent Flow Data for Machine Learning Sardar, Mohammed Zimoń, Małgorzata J. Draycott, Samuel Revell, Alistair Skillen, Alex Fluid Dynamics This article describes some common issues encountered in the use of Direct Numerical Simulation (DNS) turbulent flow data for machine learning. We focus on two specific issues; 1) the requirements for a fair validation set, and 2) the pitfalls in downsampling DNS data before training. We attempt to shed light on the impact these issues can have on machine learning and computer vision for turbulence. Further, we include statistical and spectral analysis for the homogenous isotropic turbulence from the John Hopkins Turbulence Database, a Kolmogorov flow, and a Rayleigh-Bénard Convection Cell using data generated by the authors, to concretely demonstrate these issues. |
| title | Concerning the Use of Turbulent Flow Data for Machine Learning |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2412.06050 |