PIRSA:17120015

Video URL

https://pirsa.org/17120015

The Big Data Approach to Quantum Gravity

Cunningham, W. (2017). The Big Data Approach to Quantum Gravity. Perimeter Institute for Theoretical Physics. https://pirsa.org/17120015

Cunningham, William. The Big Data Approach to Quantum Gravity. Perimeter Institute for Theoretical Physics, Dec. 14, 2017, https://pirsa.org/17120015 

          @misc{ scivideos_PIRSA:17120015,
            doi = {10.48660/17120015},
            url = {https://pirsa.org/17120015},
            author = {Cunningham, William},
            keywords = {Quantum Gravity},
            language = {en},
            title = {The Big Data Approach to Quantum Gravity},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2017},
            month = {dec},
            note = {PIRSA:17120015 see, \url{https://scivideos.org/index.php/pirsa/17120015}}
          }

William Cunningham Perimeter Institute for Theoretical Physics

Talk numberPIRSA:17120015
DOI10.48660/17120015
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

In both Causal Set Quantum Gravity as well as in the String Landscape, we face the challenging tasks of sifting through large state spaces and searching for the set of solutions which best model our physical universe. I demonstrate in this talk how efficient parallel algorithms can give us access to areas of physics previously unstudied due to computational barriers. I first present new methods to accelerate the evolution of causal set Markov chains, which enables us to look for the spontaneous emergence of manifoldlike structure. Then, I use a similar graph theoretic approach to show how a dynamic vacuum selection mechanism naturally emerges in F-Theory and Type IIB String Theory in the context of eternal inflation. We will discuss in detail relevant numerical aspects, including practical limitations and algorithms from graph theory, and how machine learning can be used in future work.