City Research Online

Machine-learning the string landscape

He, Y. (2017). Machine-learning the string landscape. Physics Letters B, 774, pp. 564-568. doi: 10.1016/j.physletb.2017.10.024


We propose a paradigm to apply machine learning various databases which have emerged in the study of the string landscape. In particular, we establish 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, using a novel framework of recasting geometrical and physical data as pixelated images. We find 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, whereby rendering the paradigm a valuable tool in physics as well as pure mathematics.

Publication Type: Article
Departments: School of Science & Technology > Mathematics
SWORD Depositor:
[thumbnail of Machine_learning the string landscape.pdf]
Text - Published Version
Available under License Creative Commons Attribution.

Download (344kB) | Preview


Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email


Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login