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Inferring turbulent environments via machine learning

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Abstract

The problem of classifying turbulent environments from partial observation is key for some theoretical and applied fields, from engineering to earth observation and astrophysics, e.g., to precondition searching of optimal control policies in different turbulent backgrounds, to predict the probability of rare events and/or to infer physical parameters labeling different turbulent setups. To achieve such goal one can use different tools depending on the system’s knowledge and on the quality and quantity of the accessible data. In this context, we assume to work in a model-free setup completely blind to all dynamical laws, but with a large quantity of (good quality) data for training. As a prototype of complex flows with different attractors, and different multi-scale statistical properties we selected 10 turbulent ‘ensembles’ by changing the rotation frequency of the frame of reference of the 3d domain and we suppose to have access to a set of partial observations limited to the instantaneous kinetic energy distribution in a 2d plane, as it is often the case in geophysics and astrophysics. We compare results obtained by a machine learning (ML) approach consisting of a state-of-the-art deep convolutional neural network (DCNN) against Bayesian inference which exploits the information on velocity and entropy moments. First, we discuss the supremacy of the ML approach, presenting also results at changing the number of training data and of the hyper-parameters. Second, we present an ablation study on the input data aimed to perform a ranking on the importance of the flow features used by the DCNN, helping to identify the main physical contents used by the classifier. Finally, we discuss the main limitations of such data-driven methods and potential interesting applications.

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Data Availability

The datasets generated during and/or analyzed during the current study are available in the Smart-Turb repository, http://smart-turb.roma2.infn.it.

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Acknowledgements

The authors thank Prof. Luca Biferale for inspiration and many useful discussions. This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 882340), and by the project Beyond Borders (CUP): E84I19002270005 funded by the University of Rome Tor Vergata.

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  1. Department of Physics and INFN, University of Rome ‘Tor Vergata’, Via della Ricerca Scientifica 1, 00133, Rome, Italy

    Michele Buzzicotti & Fabio Bonaccorso

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  1. Michele Buzzicotti
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Correspondence to Michele Buzzicotti.

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Quantitative AI in Complex Fluids and Complex Flows: Challenges and Benchmarks. Guest editors: Luca Biferale, Michele Buzzicotti, Massimo Cencini. .

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Buzzicotti, M., Bonaccorso, F. Inferring turbulent environments via machine learning. Eur. Phys. J. E 45, 102 (2022). https://doi.org/10.1140/epje/s10189-022-00258-3

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