arXiv:2002.00790 Abstract | arXiv Analytics

arXiv:2002.00790 [stat.ML]Abstract References Reviews Resources

Data-Driven Discovery of Coarse-Grained Equations

Published 2020-01-30Version 1

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.

Categories: stat.ML, cs.LG, cs.NA, math.NA

Keywords: data-driven discovery, coarse-grained equations, sparse linear regression, monte carlo simulations, learning probability density function

Related articles: Most relevant | Search more

arXiv:2408.01336 [stat.ML] (Published 2024-08-02)

Sparse Linear Regression when Noises and Covariates are Heavy-Tailed and Contaminated by Outliers

Takeyuki Sasai, Hironori Fujisawa

arXiv:1503.08348 [stat.ML] (Published 2015-03-28)

Sparse Linear Regression With Missing Data

Ravi Ganti, Rebecca M. Willett

arXiv:1608.02549 [stat.ML] (Published 2016-08-08)

Sampling Requirements and Accelerated Schemes for Sparse Linear Regression with Orthogonal Least-Squares

Abolfazl Hashemi, Haris Vikalo