• DOI:10.1016/J.PHYSLETB.2017.10.024
Corpus ID: 73567760

Machine-learning the string landscape

@article{He2017MachinelearningTS,
  title={Machine-learning the string landscape},
  author={Yanghui He},
  journal={Physics Letters B},
  year={2017},
  volume={774},
  pages={564-568}
}

Yanghui He

Published 2017

Physics

Physics Letters B

Abstract 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… CONTINUE READING

View via Publisher

doi.org

Save to Library

Create Alert

Cite

Launch Research Feed