Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915123972538368 |
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| author | Straat, Michiel Markmann, Thorben Hammer, Barbara |
| author_facet | Straat, Michiel Markmann, Thorben Hammer, Barbara |
| contents | We train Fourier Neural Operator (FNO) surrogate models for Rayleigh-Bénard Convection (RBC), a model for convection processes that occur in nature and industrial settings. We compare the prediction accuracy and model properties of FNO surrogates to two popular surrogates used in fluid dynamics: the Dynamic Mode Decomposition and the Linearly-Recurrent Autoencoder Network. We regard Direct Numerical Simulations (DNS) of the RBC equations as the ground truth on which the models are trained and evaluated in different settings. The FNO performs favorably when compared to the DMD and LRAN and its predictions are fast and highly accurate for this task. Additionally, we show its zero-shot super-resolution ability for the convection dynamics. The FNO model has a high potential to be used in downstream tasks such as flow control in RBC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16209 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators Straat, Michiel Markmann, Thorben Hammer, Barbara Fluid Dynamics Machine Learning We train Fourier Neural Operator (FNO) surrogate models for Rayleigh-Bénard Convection (RBC), a model for convection processes that occur in nature and industrial settings. We compare the prediction accuracy and model properties of FNO surrogates to two popular surrogates used in fluid dynamics: the Dynamic Mode Decomposition and the Linearly-Recurrent Autoencoder Network. We regard Direct Numerical Simulations (DNS) of the RBC equations as the ground truth on which the models are trained and evaluated in different settings. The FNO performs favorably when compared to the DMD and LRAN and its predictions are fast and highly accurate for this task. Additionally, we show its zero-shot super-resolution ability for the convection dynamics. The FNO model has a high potential to be used in downstream tasks such as flow control in RBC. |
| title | Solving Turbulent Rayleigh-Bénard Convection using Fourier Neural Operators |
| topic | Fluid Dynamics Machine Learning |
| url | https://arxiv.org/abs/2501.16209 |