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Data-Driven Discovery of Coarse-Grained Equations

01/30/2020

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by Joseph Bakarji, et al.

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A general method for learning probability density function (PDF) equations based on Monte Carlo simulations of random fields is proposed. Sparse linear regression is used to discover the relevant terms of a partial differential equation of the distribution. The various properties of PDF equations, like smoothness and conservation, makes them very well adapted to equation learning methods. The results show a promising direction for data-driven discovery of coarse-grained equations in general.

Joseph Bakarji
4 publications
Daniel M. Tartakovsky
20 publications

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