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Electron tomography is a powerful tool for understanding the morphology of materials in three dimensions, but conventional reconstruction algorithms typically suffer from missing-wedge artifacts and data misalignment imposed by experimental…

Image and Video Processing · Electrical Eng. & Systems 2025-12-10 Cedric Lim , Corneel Casert , Arthur R. C. McCray , Serin Lee , Andrew Barnum , Jennifer Dionne , Colin Ophus

We present a simple method for summing so-called parquet diagrams of fermionic many-body systems with competing instabilities using the functional renormalization group. Our method is based on partial bosonization of the interaction…

Strongly Correlated Electrons · Physics 2015-09-16 Philipp Lange , Casper Drukier , Anand Sharma , Peter Kopietz

Precise algorithms capable of providing controlled solutions in the presence of strong interactions are transforming the landscape of quantum many-body physics. Particularly exciting breakthroughs are enabling the computation of non-zero…

An estimate on the operator norm of an abstract fermionic renormalization group map is derived. This abstract estimate is applied in another paper to construct the thermodynamic Green's functions of a two dimensional, weakly coupled fermion…

Mathematical Physics · Physics 2007-05-23 Joel Feldman , Horst Knoerrer , Eugene Trubowitz

The Green's function Monte Carlo (GFMC) method provides accurate solutions to the nuclear many-body problem and predicts properties of light nuclei starting from realistic two- and three-body interactions. Controlling the GFMC fermion-sign…

Nuclear Theory · Physics 2023-04-07 Gurtej Kanwar , Alessandro Lovato , Noemi Rocco , Michael Wagman

We propose to use Ramsey interferometry and single-site addressability, available in synthetic matter such as cold atoms or trapped ions, to measure real-space and time resolved spin correlation functions. These correlation functions…

Renormalization group methods are well-established tools for the (numerical) investigation of the low-energy properties of correlated quantum many-body systems, allowing to capture their scale-dependent nature. The functional…

Strongly Correlated Electrons · Physics 2022-06-09 Dominik Kiese , Tobias Mueller , Yasir Iqbal , Ronny Thomale , Simon Trebst

Quantum many-body theory has witnessed tremendous progress in various fields, ranging from atomic and solid-state physics to quantum chemistry and nuclear structure. Due to the inherent computational burden linked to the ab initio treatment…

We propose a new joint image reconstruction method by recovering edge directly from observed data. More specifically, we reformulate joint image reconstruction with vectorial total-variation regularization as an $l_1$ minimization problem…

Numerical Analysis · Mathematics 2017-12-11 Yunmei Chen , Ruogu Fang , Xiaojing Ye

We apply a functional implementation of the field-theoretical renormalization group (RG) method up to two loops to the single-impurity Anderson model. To achieve this, we follow a RG strategy similar to that proposed by Vojta \emph{et al.}…

Strongly Correlated Electrons · Physics 2012-01-11 Hermann Freire , Eberth Corrêa

We first discuss two-body and chain regularization methods for direct N-body simulations on HARP-2 and GRAPE-6. The former is used for accurate integration of perturbed binaries and hierarchies, whereas the latter deals with strong…

Astrophysics · Physics 2007-05-23 Sverre J. Aarseth

Ab initio nuclear many-body frameworks require extensive computational resources, especially when targeting heavier nuclei. Importance-truncation (IT) techniques allow to significantly reduce the dimensionality of the problem by neglecting…

Nuclear Theory · Physics 2022-03-31 J. Hoppe , A. Tichai , M. Heinz , K. Hebeler , A. Schwenk

A biorthonormal-block density-matrix renormalization group algorithm is proposed to accurately compute properties of large-scale non-Hermitian many-body systems, in which a renormalized-space partition of the non-Hermitian reduced density…

Strongly Correlated Electrons · Physics 2025-07-08 Peigeng Zhong , Wei Pan , Haiqing Lin , Xiaoqun Wang , Shijie Hu

In this paper, we study a special type of cutoff regularization in the coordinate representation. We show how this approach unites such concepts and properties as an explicit cut, a spectral representation, a homogenization, and a…

High Energy Physics - Theory · Physics 2022-12-20 A. V. Ivanov

We formulate a theory of the many-body localization transition based on a novel real space renormalization group (RG) approach. The results of this theory are corroborated and intuitively explained with a phenomenological effective…

Disordered Systems and Neural Networks · Physics 2015-09-21 Ronen Vosk , David A. Huse , Ehud Altman

The open-source library, irbasis, provides easy-to-use tools for two sets of orthogonal functions named intermediate representation (IR). The IR basis enables a compact representation of the Matsubara Green's function and efficient…

Computational Physics · Physics 2019-06-28 Naoya Chikano , Kazuyoshi Yoshimi , Junya Otsuki , Hiroshi Shinaoka

We study the limits of the energy resolution that can be achieved in the calculations of spectral functions of quantum impurity models using the numerical renormalization group (NRG) technique with interleaving (z-averaging). We show that…

Strongly Correlated Electrons · Physics 2009-03-05 Rok Zitko , Thomas Pruschke

Wilson's numerical renormalization group (NRG) method for the calculation of dynamic properties of impurity models is generalized to investigate the effective impurity model of the dynamical mean field theory at finite temperatures. We…

Strongly Correlated Electrons · Physics 2009-10-31 R. Bulla , T. A. Costi , D. Vollhardt

Recent developments of experimental techniques in the field of ultra-cold gases open a path to study the crossover from 'few' to 'many' on the quantum level. In this case, accurate description of inter-particle correlations is very…

Quantum Gases · Physics 2018-03-23 Marcin Płodzień , Dariusz Wiater , Andrzej Chrostowski , Tomasz Sowiński

We present an alternative approach to decompose non-negative tensors, called many-body approximation. Traditional decomposition methods assume low-rankness in the representation, resulting in difficulties in global optimization and target…

Machine Learning · Statistics 2023-10-31 Kazu Ghalamkari , Mahito Sugiyama , Yoshinobu Kawahara
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