Deep-Learning the Landscape

Yang-Hui He proposes a paradigm to deep-learn the ever-expanding databases which have emerged in mathematical physics and particle phenomenology, as diverse as the statistics of string vacua or combinatorial and algebraic geometry. As concrete examples, he establishes multi-layer neural networks as both classifiers and predictors and train them with a host of available data ranging from Calabi-Yau manifolds and vector bundles, to quiver representations for gauge theories. He finds that even a relatively simple neural network can learn many significant quantities to astounding accuracy in a matter of minutes and can also predict hithertofore unencountered results. This paradigm should prove a valuable tool in various investigations in landscapes in physics as well as pure mathematics.


[amazon_link asins=’B00C8UQZAO,0786684887,0521322650,0898714788′ template=’ProductCarousel’ store=’faculti-21′ marketplace=’UK’ link_id=’d4667340-d477-11e7-9c60-b3350403e186′]


Image courtesy of interviewee

Log-in or Sign-up to Faculti
Currently viewing this subject insight as a guest. You have insight(s) remaining for this month.

Leave a Reply

Your email address will not be published.

Copyright © Faculti Media Limited 2013 - 2024. All rights reserved.