no code implementations • 20 Nov 2023 • Harold Erbin, Riccardo Finotello

We review advancements in deep learning techniques for complete intersection Calabi-Yau (CICY) 3- and 4-folds, with the aim of understanding better how to handle algebraic topological data with machine learning.

1 code implementation • 22 Dec 2022 • Harold Erbin, Vincent Lahoche, Dine Ousmane Samary

A statistical ensemble of neural networks can be described in terms of a quantum field theory (NN-QFT correspondence).

1 code implementation • 16 Nov 2022 • Harold Erbin, Atakan Hilmi Fırat

The geometry of 4-string contact interaction of closed string field theory is characterized using machine learning.

2 code implementations • 4 Aug 2021 • Harold Erbin, Riccardo Finotello, Robin Schneider, Mohamed Tamaazousti

We continue earlier efforts in computing the dimensions of tangent space cohomologies of Calabi-Yau manifolds using deep learning.

1 code implementation • 3 Aug 2021 • Harold Erbin, Vincent Lahoche, Dine Ousmane Samary

A major result of our analysis is that changing the standard deviation of the neural network weight distribution can be interpreted as a renormalization flow in the space of networks.

1 code implementation • 30 Jul 2020 • Harold Erbin, Riccardo Finotello

99%) accuracy for $h^{1, 1}$ using a neural network inspired by the Inception model for the old dataset, using only 30% (resp.

2 code implementations • 27 Jul 2020 • Harold Erbin, Riccardo Finotello

We introduce a neural network inspired by Google's Inception model to compute the Hodge number $h^{1, 1}$ of complete intersection Calabi-Yau (CICY) 3-folds.

no code implementations • 16 Jun 2020 • M. N. Chernodub, Harold Erbin, V. A. Goy, A. V. Molochkov

We investigate the advantages of machine learning techniques to recognize the dynamics of topological objects in quantum field theories.

no code implementations • 18 Nov 2019 • M. N. Chernodub, Harold Erbin, I. V. Grishmanovskii, V. A. Goy, A. V. Molochkov

Vacuum fluctuations of quantum fields between physical objects depend on the shapes, positions, and internal composition of the latter.

no code implementations • 6 Sep 2018 • Harold Erbin, Sven Krippendorf

In this case, the machine knows consistent examples of supersymmetric field theories with a single field and generates new examples of such theories.

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