Machine learning approaches for new insight on fundamental mathematical and physical constants

Project Title:Machine learning approaches for new insight on fundamental mathematical and physical constants

Supervisor(s):Prof. Ido Kaminer

Abstract:

Physics and math have many fundamental constants that appear naturally in completely different processes, yet are sometimes related in surprising ways. Examples appear in self-similarity of fractals in chaos theory, phase transitions in percolation processes, and other critical parameters in statistical physics, as well as in combinatorics and number theory.

The project’s goal is to develop methodical methods to find new mathematical connections between seemingly unrelated constants by applying advanced statistical and machine-learning algorithms.

This may lead to new equations in physics and mathematics that can help us “reverse engineer” new laws of nature