Black Holes and the loss landscape in machine learning
A bstract Understanding the loss landscape is an important problem in machine learning.
One key feature of the loss function, common to many neural network architectures, is the …
One key feature of the loss function, common to many neural network architectures, is the …
The r-matrix net
We provide a novel Neural Network architecture that can: i) output R-matrix for a given
quantum integrable spin chain, ii) search for an integrable Hamiltonian and the …
quantum integrable spin chain, ii) search for an integrable Hamiltonian and the …
Machine learning of phases and structures for model systems in physics
D Bayo, B Çivitcioğlu, JJ Webb, A Honecker… - arXiv preprint arXiv …, 2024 - arxiv.org
The detection of phase transitions is a fundamental challenge in condensed matter physics,
traditionally addressed through analytical methods and direct numerical simulations. In …
traditionally addressed through analytical methods and direct numerical simulations. In …
Confinement and phase transition in lattice U (1) gauge theory and machine learning analysis
박찬주 - 2022 - s-space.snu.ac.kr
Using lattice formalism, we can show that there exists confining phase in the strong coupling
limit for the compact U (1) gauge theory. Since we know that there is no confining behavior …
limit for the compact U (1) gauge theory. Since we know that there is no confining behavior …