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Scaling concepts and renormalization group (RG) methods are applied to a simple linear model of human posture control consisting of a trembling or quivering string subject to damping and restoring forces. The string is driven by…

Condensed Matter · Physics 2009-10-31 Francisco Alonso-Sanchez , David Hochberg

A major advance in density-matrix renormalization group (DMRG) calculations has been achieved by the invention of highly efficient DMRG techniques for the simulation of real-time dynamics of strongly correlated quantum systems in one…

Strongly Correlated Electrons · Physics 2007-05-23 U. Schollwoeck , S. R. White

In this paper we summarize the minimal supersymmetric standard model as well as the renormalization group equations of its parameters. We proceed to examine the feasability of the model when the breaking of supersymmetry is parametrized by…

High Energy Physics - Phenomenology · Physics 2009-10-22 D. J. Castano , E. J. Piard , P. Ramond

We introduce a class of variational states to describe quantum many-body systems. This class generalizes matrix product states which underly the density-matrix renormalization group approach by combining them with weighted graph states.…

Quantum Physics · Physics 2009-11-13 R. Hübener , C. Kruszynska , L. Hartmann , W. Dür , F. Verstraete , J. Eisert , M. B. Plenio

I show how a renormalization group (RG) method can be used to incrementally integrate the information in cosmological large-scale structure data sets (including CMB, galaxy redshift surveys, etc.). I show numerical tests for Gaussian…

Cosmology and Nongalactic Astrophysics · Physics 2019-03-06 Patrick McDonald

We study Radiative Electroweak Symmetry Breaking in the Minimal Supersymmetric Standard Model (MSSM). We employ the 2-loop Renormalization Group equations for running masses and couplings taking into account sparticle threshold effects. The…

High Energy Physics - Phenomenology · Physics 2009-09-24 A. Dedes , A. B. Lahanas , K. Tamvakis

We investigate the application of the Density Matrix Renormalization Group (DMRG) to the Hubbard model in momentum-space. We treat the one-dimensional models with dispersion relations corresponding to nearest-neighbor hopping and $1/r$…

Strongly Correlated Electrons · Physics 2009-11-07 Satoshi Nishimoto , Eric Jeckelmann , Florian Gebhard , Reinhard M. Noack

As a quantum-informative window into quantum many-body physics, the concept and application of entanglement renormalization group (ERG) have been playing a vital role in the study of novel quantum phases of matter, especially long-range…

Quantum Physics · Physics 2023-03-31 Meng-Yuan Li , Peng Ye

Inspired by the superblock method of White, we introduce a simple modification of the standard Renormalization Group (RG) technique for the study of quantum lattice systems. Our method which takes into account the effect of Boundary…

Statistical Mechanics · Physics 2009-10-28 A. Langari , V. Karimipour

In recent years, reduced basis methods (RBMs) have been adapted to the many-body eigenvalue problem and they have been used, largely in nuclear physics, as fast emulators able to bypass expensive direct computations while still providing…

Superconductivity · Physics 2023-04-19 Virgil V. Baran , Denis R. Nichita

We consider a generic class of effective quantum field theories with arbitrary gauge groups and scalar matter fields. In such theories, we derive the one-loop Renormalization Group Equations (RGEs) for the physical dimension-six operators.…

High Energy Physics - Phenomenology · Physics 2025-03-25 Mikołaj Misiak , Ignacy Nałęcz

A class of models that have been widely used are the exponential random graph (ERG) models, which form a comprehensive family of models that include independent and dyadic edge models, Markov random graphs, and many other graph…

Statistics Theory · Mathematics 2022-02-07 Denise Duarte , Rafael Honório Pereira Alves

Regime shifts in high-dimensional time series arise naturally in many applications, from neuroimaging to finance. This problem has received considerable attention in low-dimensional settings, with both Bayesian and frequentist methods used…

Methodology · Statistics 2025-09-05 Xiudi Li , Abolfazl Safikhani , Ali Shojaie

We develop a dynamic field-theoretic renormalization-group (RG) theory for the cooling first-order phase transitions in the Potts model. It is suggested that the well-known imaginary fixed points of the $q$-state Potts model for $q>10/3$ in…

Statistical Mechanics · Physics 2017-03-22 Ning Liang , Fan Zhong

We develop a Renormalization Group (RG) approach to the study of existence and uniqueness of solutions to stochastic partial differential equations driven by space-time white noise. As an example we prove well-posedness and independence of…

Probability · Mathematics 2015-02-20 Antti Kupiainen

In this paper, inspired by the Costello's seminal work, we present a general formulation of exact renormalization group (RG) within the Batalin-Vilkovisky (BV) quantization scheme. In the spirit of effective field theory, the BV bracket and…

High Energy Physics - Theory · Physics 2018-04-18 Roberto Zucchini

Entanglement renormalization is a real-space renormalization group (RG) transformation for quantum many-body systems. It generates the multi-scale entanglement renormalization ansatz (MERA), a tensor network capable of efficiently…

Strongly Correlated Electrons · Physics 2015-06-15 Sukhwinder Singh , Guifre Vidal

The density-matrix renormalization-group (DMRG) algorithm is extended to treat time-dependent problems. The method provides a systematic and robust tool to explore out-of-equilibrium phenomena in quantum many-body systems. We illustrate the…

Mesoscale and Nanoscale Physics · Physics 2009-11-07 M. A. Cazalilla , J. B. Marston

The discrete chemical master equation (dCME) provides a general framework for studying stochasticity in mesoscopic reaction networks. Since its direct solution rapidly becomes intractable due to the increasing size of the state space,…

Quantitative Methods · Quantitative Biology 2017-07-27 Youfang Cao , Anna Terebus , Jie Liang

Robust estimators for generalized linear models (GLMs) are not easy to develop due to the nature of the distributions involved. Recently, there has been growing interest in robust estimation methods, particularly in contexts involving a…

Methodology · Statistics 2025-07-08 Marina Valdora , Claudio Agostinelli