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We present a renormalization group (RG) procedure which works naturally on a wide class of interacting one-dimension models based on perturbed (possibly strongly) continuum conformal and integrable models. This procedure integrates Kenneth…

Strongly Correlated Electrons · Physics 2016-07-05 Robert M. Konik , Yury Adamov

We have devised and implemented a local ab initio Density Matrix Renormalization Group (DMRG) algorithm to describe multireference nondynamic correlations in large systems. For long molecules that are extended in one of their spatial…

Strongly Correlated Electrons · Physics 2009-11-11 Johannes Hachmann , Wim Cardoen , Garnet Kin-Lic Chan

We show that an appropriate description of the non-equilibrium dynamics of disordered systems is obtained through a strong disorder renormalization procedure in {\it configuration space}, that we define for any master equation with…

Disordered Systems and Neural Networks · Physics 2008-10-08 Cecile Monthus , Thomas Garel

We extend the real-space renormalization group (RG) approach to the study of the energy level statistics at the integer quantum Hall (QH) transition. Previously it was demonstrated that the RG approach reproduces the critical distribution…

Disordered Systems and Neural Networks · Physics 2009-11-07 Philipp Cain , Rudolf A. Roemer , Mikhail E. Raikh

Renormalization group methods are used to study the low-energy behavior of the unscreened Coulomb interaction in a one-dimensional electron system. By applying a GW approximation, a strong wavefunction renormalization is found in the model,…

Strongly Correlated Electrons · Physics 2007-05-23 S. Bellucci , J. Gonzalez

Sampling equilibrium molecular configurations from the Boltzmann distribution is a longstanding challenge. Boltzmann Generators (BGs) address this by combining exact-likelihood generative models with importance sampling, but practical…

Machine Learning · Computer Science 2026-05-29 Weilong Chen , Bojun Zhao , Jan Eckwert , Julija Zavadlav

The renormalization group (RG) is a powerful theoretical framework developed to consistently transform the description of configurations of systems with many degrees of freedom, along with the associated model parameters and coupling…

Statistical Mechanics · Physics 2026-04-20 Andrea Gabrielli , Diego Garlaschelli , Subodh P. Patil , M. Ángeles Serrano

The renormalization group is the cornerstone of the modern theory of universality and phase transitions, a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its network counterpart is…

Statistical Mechanics · Physics 2023-01-11 Pablo Villegas , Tommaso Gili , Guido Caldarelli , Andrea Gabrielli

We investigate how additive weak noise (correlated as well as uncorrelated) modifies the parameters of the Gray-Scott (GS) reaction diffusion system by performing numerical simulations and applying a Renormalization Group (RG) analysis in…

Other Condensed Matter · Physics 2007-05-23 M. -P. Zorzano , D. Hochberg , F. Moran

We present a recently-developed renormalization group scheme, the functional renormalization group (fRG), as a many-particle method suited to account for the two-particle interactions between the electrons in complex quantum dot geometries.…

Strongly Correlated Electrons · Physics 2007-05-23 C. Karrasch

The Density Matrix Renormalization Group (DMRG) has become a powerful numerical method that can be applied to low-dimensional strongly correlated fermionic and bosonic systems. It allows for a very precise calculation of static, dynamical…

Condensed Matter · Physics 2007-05-23 Karen Hallberg

Numerical renormalization group (NRG) calculations of quantum impurity models, based on a logarithmic discretization in energy of electronic or bosonic Hamiltonians, provide a powerful tool to describe physics involving widely separated…

Strongly Correlated Electrons · Physics 2009-11-13 Axel Freyn , Serge Florens

The renormalization group plays an essential role in many areas of physics, both conceptually and as a practical tool to determine the long-distance low-energy properties of many systems on the one hand and on the other hand search for…

Statistical Mechanics · Physics 2021-05-10 N. Dupuis , L. Canet , A. Eichhorn , W. Metzner , J. M. Pawlowski , M. Tissier , N. Wschebor

The Density Matrix Renormalization Group (DMRG) has become a powerful numerical method that can be applied to low-dimensional strongly correlated fermionic and bosonic systems. It allows for a very precise calculation of static, dynamic and…

Strongly Correlated Electrons · Physics 2008-11-26 Karen Hallberg

We show that the Wilsonian formulation of the renormalization group (RG) defines a quantum channel acting on the momentum-space density matrices of a quantum field theory. This information theoretical property of the RG allows us to derive…

High Energy Physics - Theory · Physics 2023-06-28 Matheus H. Martins Costa , Jeroen van den Brink , Flavio S. Nogueira , Gastão I. Krein

The interactions between climate and the environment are highly complex. Due to this complexity, process-based models are often preferred to estimate the net magnitude and directionality of interactions in the Earth System. However, these…

Atmospheric and Oceanic Physics · Physics 2015-09-30 Erik Casagrande , Brigitte Mueller , Diego Miralles , Dara Entekhabi , Annalisa Molini

The Density Matrix Renormalization Group (DMRG) has become a powerful numerical method that can be applied to low-dimensional strongly correlated fermionic and bosonic systems. It allows for a very precise calculation of static, dynamic and…

Condensed Matter · Physics 2007-05-23 Karen Hallberg

Building on recent progress in the study of Anderson and many-body localization via the renormalization group (RG), we examine the scaling theory of localization in the quantum Random Energy Model (QREM). The QREM is known to undergo a…

Disordered Systems and Neural Networks · Physics 2026-03-27 Federico Balducci , Giacomo Bracci-Testasecca , Jacopo Niedda , Antonello Scardicchio , Carlo Vanoni

The multivariate hypergeometric distribution describes sampling without replacement from a discrete population of elements divided into multiple categories. Addressing a gap in the literature, we tackle the challenge of estimating discrete…

Machine Learning · Computer Science 2024-06-11 Liam Hodgson , Danilo Bzdok

Renormalization group (RG) methods are emerging as tools in biology and computer science to support the search for simplifying structure in distributions over high-dimensional spaces. We show that mixture models can be thought of as having…

Statistical Mechanics · Physics 2024-02-09 Adam G. Kline , Stephanie E. Palmer