Universes as Bigdata: Superstrings, Calabi-Yau Manifolds and Machine-Learning

Abstract: We review how historically the problem of string phenomenology lead theoretical physics first to algebraic/differential geometry, and then to computational geometry, and now to data science and AI.

With the concrete playground of the Calabi-Yau landscape, accumulated by the collaboration of physicists, mathematicians, and computer scientists over the last 4 decades, we show how the latest techniques in machine-learning can help explore problems of physical and mathematical interest, from geometry to group theory, to combinatorics and number theory.

Mathematics

Audience: researchers in the topic

Seminario de Geometría y Física - Matemática UCN-USP