90 citations · 251 across the 11 of their papers we have counts for
6 papers · 1 filter
Phase Transitions in Particle Physics -- Results and Perspectives from Lattice Quantum Chromo-Dynamics
Gert Aarts, Joerg Aichelin, Chris Allton +38
Phase transitions in a non-perturbative regime can be studied by ab initio Lattice Field Theory methods. The status and future research directions for LFT investigations of Quantum…
Reducing finite-size effects with reweighted renormalization group transformations
Dimitrios Bachtis
We combine histogram reweighting techniques with the two-lattice matching Monte Carlo renormalization group method to conduct computationally efficient calculations of critical exp…
Interpreting machine learning functions as physical observables
Gert Aarts, Dimitrios Bachtis, Biagio Lucini
We propose to interpret machine learning functions as physical observables, opening up the possibility to apply "standard" statistical-mechanical methods to outputs from neural net…
Inverse Renormalization Group in Quantum Field Theory
Dimitrios Bachtis, Gert Aarts, Francesco Di Renzo +1
We propose inverse renormalization group transformations within the context of quantum field theory that produce the appropriate critical fixed point structure, give rise to invers…
Quantum field-theoretic machine learning
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We derive machine learning algorithms from discretized Euclidean field theories, making inference and learning possible within dynamics described by quantum field theory. Specifica…
Adding machine learning within Hamiltonians: Renormalization group transformations, symmetry breaking and restoration
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We present a physical interpretation of machine learning functions, opening up the possibility to control properties of statistical systems via the inclusion of these functions in…