Data-Driven Discovery of Coarse-Grained Equations

30 Jan 2020Joseph BakarjiDaniel M. Tartakovsky

We introduce a general method for learning probability density function (PDF) equations from Monte Carlo simulations of partial differential equations with uncertain (random) parameters and forcings. The method relies on sparse linear regression to discover the relevant terms in the PDF equation... (read more)

PDF Abstract

Code


No code implementations yet. Submit your code now

Tasks


Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.