Search Results for author:
Found 10 papers, 6 papers with code
Deep learning complete intersection Calabi-Yau manifolds
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.
Renormalization in the neural network-quantum field theory correspondence
A statistical ensemble of neural networks can be described in terms of a quantum field theory (NN-QFT correspondence).
Characterizing 4-string contact interaction using machine learning
The geometry of 4-string contact interaction of closed string field theory is characterized using machine learning.
Deep multi-task mining Calabi-Yau four-folds
We continue earlier efforts in computing the dimensions of tangent space cohomologies of Calabi-Yau manifolds using deep learning.
Nonperturbative renormalization for the neural network-QFT correspondence
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.
Machine learning for complete intersection Calabi-Yau manifolds: a methodological study
99%) accuracy for $h^{1, 1}$ using a neural network inspired by the Inception model for the old dataset, using only 30% (resp.
Inception Neural Network for Complete Intersection Calabi-Yau 3-folds
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.
Topological defects and confinement with machine learning: the case of monopoles in compact electrodynamics
We investigate the advantages of machine learning techniques to recognize the dynamics of topological objects in quantum field theories.
Casimir effect with machine learning
Vacuum fluctuations of quantum fields between physical objects depend on the shapes, positions, and internal composition of the latter.
GANs for generating EFT models
In this case, the machine knows consistent examples of supersymmetric field theories with a single field and generates new examples of such theories.
