Machine Learning Detects Terminal Singularities in Q-Fano Varieties
Offered By: Applied Algebraic Topology Network via YouTube
Course Description
Overview
Explore the intersection of machine learning and algebraic geometry in this 48-minute talk from the Applied Algebraic Topology Network. Delve into the application of neural networks to study Q-Fano varieties, key components in the Minimal Model Program. Learn how a high-accuracy machine learning model was developed to detect terminal singularities in toric Fano varieties, leading to two significant outcomes: the formulation of a new combinatorial criterion for determining terminal singularities in Picard rank two toric varieties, and the first glimpse into the landscape of eight-dimensional Q-Fano varieties. Gain insights into this collaborative research effort that bridges advanced mathematics and cutting-edge machine learning techniques.
Syllabus
Sara Veneziale (09/18/24): Machine learning detects terminal singularities
Taught by
Applied Algebraic Topology Network
Related Courses
Analytic Combinatorics, Part IPrinceton University via Coursera Analytic Combinatorics, Part II
Princeton University via Coursera Analytic Combinatorics
Princeton University via Coursera Principles of Computing (Part 1)
Rice University via Coursera Combinatorics and Probability
Moscow Institute of Physics and Technology via Coursera