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Machine Learning and String theory

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One of the holy grails of modern theoretical physics is the unification of Quantum Mechanics with Einstein’s relativity. String theory is the only known consistent theory of quantum gravity, and arguably the most promising candidate for a unified theory of physics. Since its inception in the late 1960s, it has provided tremendous insights into our understanding of the physical world, and has overseen many interesting developments in various branches of pure mathematics and theoretical physics. Despite string theory’s many successes, a string model that explains all observed data from cosmology and particle physics experiments, has eluded discovery. This is owing to the particularly large landscape of valid string theory solutions, estimated to be of the size 10^{270,000}. Most of these solutions are thought to lead to descriptions of universes that do not resemble ours in detail.

String theory posits extra-dimensions of space. These are often described by complex geometries called Calabi—Yau manifolds. In this talk, I will describe recent progress in utilising machine learning in modelling topological and geometric properties of such manifolds, and showcase how it brings us closer to understanding quantum gravity with the help of machine learning. I will also describe a second example of how simple tools from machine learning can be used to predict properties of hadronic matter in the realm of Quantum Chromodynamics.

This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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