Model-Driven Conditional Fourier Neural Operator for Spectrum-Consistent Synthetic Turbulence Generation
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866917214981980160 |
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| author | Lin, Hongyuan Wang, Shizhao |
| author_facet | Lin, Hongyuan Wang, Shizhao |
| contents | This short note proposes a model-driven conditional Fourier neural operator (MD-CFNO) for synthetic turbulence generation. Spectrum-consistent synthetic turbulence is essential for inflow boundary construction in computational fluid dynamics and for broadband aeroacoustic noise prediction. Data-driven turbulence synthesis with neural networks has emerged as a promising direction. However, generating flow fields that match prescribed energy spectra across wide physical regimes remains challenging. Existing data-driven methods typically rely on expensive reliable datasets with limited generalization and are prone to regression-to-the-mean when trained in the spatial domain. To address these issues, the MD-CFNO is proposed with three components: a model-driven data construction strategy is adopted to improve interpretability and broaden the generalizable parameter regime; conditional stochastic generation is integrated into the Fourier neural operator architecture to alleviate regression-to-the-mean effects; and a composite loss is introduced to accelerate convergence and enhance spectral fidelity. Results show that the proposed MD-CFNO generates spectrum-consistent synthetic turbulence and achieves robust performance under both interpolation and out-of-distribution extrapolation conditions. This study provides a model-driven perspective on synthetic turbulence, showing the advantages of Fourier neural operators for conditional generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_14745 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Model-Driven Conditional Fourier Neural Operator for Spectrum-Consistent Synthetic Turbulence Generation Lin, Hongyuan Wang, Shizhao Fluid Dynamics This short note proposes a model-driven conditional Fourier neural operator (MD-CFNO) for synthetic turbulence generation. Spectrum-consistent synthetic turbulence is essential for inflow boundary construction in computational fluid dynamics and for broadband aeroacoustic noise prediction. Data-driven turbulence synthesis with neural networks has emerged as a promising direction. However, generating flow fields that match prescribed energy spectra across wide physical regimes remains challenging. Existing data-driven methods typically rely on expensive reliable datasets with limited generalization and are prone to regression-to-the-mean when trained in the spatial domain. To address these issues, the MD-CFNO is proposed with three components: a model-driven data construction strategy is adopted to improve interpretability and broaden the generalizable parameter regime; conditional stochastic generation is integrated into the Fourier neural operator architecture to alleviate regression-to-the-mean effects; and a composite loss is introduced to accelerate convergence and enhance spectral fidelity. Results show that the proposed MD-CFNO generates spectrum-consistent synthetic turbulence and achieves robust performance under both interpolation and out-of-distribution extrapolation conditions. This study provides a model-driven perspective on synthetic turbulence, showing the advantages of Fourier neural operators for conditional generation. |
| title | Model-Driven Conditional Fourier Neural Operator for Spectrum-Consistent Synthetic Turbulence Generation |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2601.14745 |