Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics
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1908.08941.pdf
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6.09 MB
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13f4204140bfcf30fcca1ff5583b8cc6
Author(s)
Arbabi, Hassan
Sapsis, Themistoklis
Date Issued
June 29, 2022
Journal
SIAM/ASA Journal on Uncertainty Quantification
Publisher
Society for Industrial & Applied Mathematics (SIAM)
Citation
Arbabi, Hassan and Sapsis, Themistoklis. 2022. "Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics." SIAM/ASA Journal on Uncertainty Quantification, 10 (2).
Version
Author's final manuscript
Abstract
Strongly nonlinear flows, which commonly arise in geophysical and engineering turbulence, are characterized by persistent and intermittent energy transfer between various spatial and temporal scales. These systems are difficult to model and analyze due to combination of high dimensionality and uncertainty, and there has been much interest in obtaining reduced models, in the form of stochastic closures, that can replicate their non-Gaussian statistics in many dimensions. Here, we propose a data-driven framework to model stationary chaotic dynamical systems through nonlinear transformations and a set of decoupled stochastic differential equations (SDEs). Specifically, we use optimal transport to find a transformation from the distribution of time-series data to a multiplicative reference probability measure such as the standard normal distribution. Then we find the set of decoupled SDEs that admit the reference measure as the invariant measure, and also closely match the spectrum of the transformed data. As such, this framework represents the chaotic time series as the evolution of a stochastic system observed through the lens of a nonlinear map. We demonstrate the application of this framework in Lorenz-96 system, a 10-dimensional model of high-Reynolds cavity flow, and reanalysis climate data. These examples show that SDE models generated by this framework can reproduce the non-Gaussian statistics of systems with moderate dimensions (e.g. 10 and more), and predict super-Gaussian tails that are not readily available from little training data. These findings suggest that this class of models provide an efficient hypothesis space for learning strongly nonlinear flows from small amounts of data.
Subjects
Applied Mathematics
Discrete Mathematics and Combinatorics
Statistics, Probability and Uncertainty
Modeling and Simulation
Statistics and Probability
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-ShareAlike
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DOI of Published Version
https://doi.org/10.1137/20m1359833
