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Résumé |
We propose 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, we establish 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. 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. This paradigm should prove a valuable tool in various investigations in landscapes in
physics as well as pure mathematics. |
