90 citations · 251 across the 11 of their papers we have counts for
11 papers
Overlap renormalization group transformations for disordered systems
Dimitrios Bachtis
We establish a renormalization group approach which is implemented on the degrees of freedom defined by the overlap of two replicas to determine the critical fixed point and to ext…
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…
Quantum field theories, Markov random fields and machine learning
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
The transition to Euclidean space and the discretization of quantum field theories on spatial or space-time lattices opens up the opportunity to investigate probabilistic machine l…
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…
Machine learning with quantum field theories
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
The precise equivalence between discretized Euclidean field theories and a certain class of probabilistic graphical models, namely the mathematical framework of Markov random field…
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…
Mapping distinct phase transitions to a neural network
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We demonstrate, by means of a convolutional neural network, that the features learned in the two-dimensional Ising model are sufficiently universal to predict the structure of symm…
Extending machine learning classification capabilities with histogram reweighting
Dimitrios Bachtis, Gert Aarts, Biagio Lucini
We propose the use of Monte Carlo histogram reweighting to extrapolate predictions of machine learning methods. In our approach, we treat the output from a convolutional neural net…