Generative diffusion models for synthetic trajectories of heavy and light particles in turbulence
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914829560709120 |
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| author | Li, Tianyi Tommasi, Samuele Buzzicotti, Michele Bonaccorso, Fabio Biferale, Luca |
| author_facet | Li, Tianyi Tommasi, Samuele Buzzicotti, Michele Bonaccorso, Fabio Biferale, Luca |
| contents | Heavy and light particles are commonly found in many natural phenomena and industrial processes, such as suspensions of bubbles, dust, and droplets in incompressible turbulent flows. Based on a recent machine learning approach using a diffusion model that successfully generated single tracer trajectories in three-dimensional turbulence and passed most statistical benchmarks across time scales, we extend this model to include heavy and light particles. Given the particle type - tracer, light, or heavy - the model can generate synthetic, realistic trajectories with correct fat-tail distributions for acceleration, anomalous power laws, and scale dependent local slope properties. This work paves the way for future exploration of the use of diffusion models to produce high-quality synthetic datasets for different flow configurations, potentially allowing interpolation between different setups and adaptation to new conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05008 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative diffusion models for synthetic trajectories of heavy and light particles in turbulence Li, Tianyi Tommasi, Samuele Buzzicotti, Michele Bonaccorso, Fabio Biferale, Luca Fluid Dynamics Heavy and light particles are commonly found in many natural phenomena and industrial processes, such as suspensions of bubbles, dust, and droplets in incompressible turbulent flows. Based on a recent machine learning approach using a diffusion model that successfully generated single tracer trajectories in three-dimensional turbulence and passed most statistical benchmarks across time scales, we extend this model to include heavy and light particles. Given the particle type - tracer, light, or heavy - the model can generate synthetic, realistic trajectories with correct fat-tail distributions for acceleration, anomalous power laws, and scale dependent local slope properties. This work paves the way for future exploration of the use of diffusion models to produce high-quality synthetic datasets for different flow configurations, potentially allowing interpolation between different setups and adaptation to new conditions. |
| title | Generative diffusion models for synthetic trajectories of heavy and light particles in turbulence |
| topic | Fluid Dynamics |
| url | https://arxiv.org/abs/2406.05008 |