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Signed-off-by: Riccardo Finotello <riccardo.finotello@gmail.com>
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Riccardo
2020-10-20 19:29:13 +02:00
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@@ -6,7 +6,7 @@ They are mainly based on published work~\cite{Finotello:2019:ClassicalSolutionBo
However I also include some hints to future directions to cover which might expand the work shown here.
The thesis is organised in three main parts plus a fourth with appendices and notes.
\Cref{part:cft} of the manuscript is dedicated to set the stage for the entire discussion and to present mathematical tools used to compute amplitudes with phenomenological relevance in string theory.
\Cref{part:cft} of the manuscript is dedicated to set the stage for the entire discussion and to present mathematical tools used to compute amplitudes with (semi-)phenomenological relevance in string theory.
Namely it starts with an introduction on conformal symmetry (clearly focusing only on aspects relevant to the discussion as many reviews on the subject have already been written) and the role of compactification and D-branes in replicating results obtained in particle physics.
Then the analysis of a specific setup involving angled D6-branes intersecting in non factorised internal space is presented.\footnotemark{}
\footnotetext{%
@@ -28,7 +28,7 @@ Namely it is hidden in contact terms and interaction with massive string states
\Cref{part:deeplearning} is dedicated to state-of-the-art application of deep learning techniques to the field of string theory compactifications.
The Hodge numbers of Complete Intersection Calabi--Yau $3$-folds are computed through a rigorous data science and machine learning analysis.
In fact the blind application of neural networks to the configuration matrix of the manifolds can be improved by exploratory data analysis and feature engineering, from which to infer behaviour and relations of topological quantities invisibly hidden in the configuration matrix.
Deep learning techniques are then applied to the manifolds to obtain the Hodge numbers as a regression task.\footnotemark{}
Deep learning techniques are then applied to the configuration matrix of the manifolds to obtain the Hodge numbers as a regression task.\footnotemark{}
\footnotetext{%
Many previous approaches have proposed classification tasks to get the best performance out of machine learning models.
This however implies specific knowledge of the definition interval of the Hodge numbers and does not generalise well to unknown examples of Complete Intersection Calabi--Yau manifolds.