Speaker
Description
In this tutorial, we introduce the AI technique of symbolic regression, combined with compressed sensing for the identification of compact, interpretable models.
Specifically, we introduce the Sure-Independence Screening and Sparsifying Operator (SISSO), together with its recent variants.
The methodology starts from a set of candidate features, provided by the user, and it builds a tree of possible mathematical expression, involving linear and nonlinear operators, up to a given complexity. A compressed sensing solver finds, among billions or trillions of candidate expressions those that better explain the training data.
We will show demonstrative applications to materials science, including prediction of perovskite-materials stability and topological-insulators identification.
