Neural network based generation of a 1-dimensional stochastic field with turbulent velocity statistics
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Abstract (arXiv)
We define and study a fully-convolutional neural network stochastic model, NN-Turb, which generates a 1-dimensional field with some turbulent velocity statistics. In particular, the generated process satisfies the Kolmogorov 2/3 law for second order structure function. It also presents negative skewness across scales (i.e. Kolmogorov 4/5 law) and exhibits intermittency as characterized by skewness and flatness. Furthermore, our model is never in contact with turbulent data and only needs the desired statistical behavior of the structure functions across scales for training.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 50 references that are in the map. First posted on arXiv 21 Nov 2022.
Builds on
11 of 50 references.
References · 11
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2015Generative Moment Matching Networkscited by 252
- 2019Towards Physics-informed Deep Learning for Turbulent Flow Predictioncited by 203
- 2018Deep Fluids: A Generative Network for Parameterized Fluid Simulationscited by 121
- 2021An Introduction to Deep Generative Modelingcited by 104
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- 2020Multi-fidelity Generative Deep Learning Turbulent Flowscited by 21
- 2022Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flowcited by 20
- 2019Learning Latent Dynamics for Partially-Observed Chaotic Systemscited by 18
- 2020Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot databasecited by 17
- 2019Learning Generalized Quasi-Geostrophic Models Using Deep Neural Numerical Modelscited by 3
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Citing papers per year: 2023: 2.
2 papers.