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We examine the challenging problem of constructing reduced models for the long time prediction of systems where there is no timescale separation between the resolved and unresolved variables. In previous work we focused on the case where…

Numerical Analysis · Mathematics 2017-07-10 Jacob Price , Panos Stinis

The renormalization method is specifically aimed at connecting theories describing physical processes at different length scales and thereby connecting different theories in the physical sciences. The renormalization method used today is…

Statistical Mechanics · Physics 2011-02-21 Leo P. Kadanoff

We develop a mathematical and numerical framework to solve state estimation problems for applications that present variations in the shape of the spatial domain. This situation arises typically in a biomedical context where inverse problems…

Numerical Analysis · Mathematics 2023-03-14 Felipe Galarce , Damiano Lombardi , Olga Mula

A central feature of complex systems is the relevance and entanglement of different levels of description. For instance, the dynamics of ecosystems can be alternatively described in terms of large ecological processes and classes of…

Populations and Evolution · Quantitative Biology 2025-10-06 Juan Giral Martínez , Silvia de Monte , Matthieu Barbier

We give an a posteriori analysis of a semi-discrete discontinuous Galerkin scheme approximating solutions to a model of multiphase elastodynamics, which involves an energy density depending not only on the strain but also the strain…

Numerical Analysis · Mathematics 2023-03-01 Jan Giesselmann , Tristan Pryer

The renormalization of the periodic potential is investigated in the framework of the Euclidean one-component scalar field theory by means of the differential RG approach. Some known results about the sine-Gordon model are recovered in an…

High Energy Physics - Theory · Physics 2009-10-31 I. Nandori , J. Polonyi , K. Sailer

The real-space renormalization group (RSRG) method introduced previously for the Brownian landscape is generalized to obtain the joint probability distribution of the subset of the important extrema at large scales of other one-dimensional…

Disordered Systems and Neural Networks · Physics 2009-11-07 P. Le Doussal , C. Monthus

We propose a new implementation of real-space renormalization group (RG) transformations for quantum states on a lattice. Key to this approach is the removal of short-ranged entanglement, similar to Vidal's entanglement renormalization…

Quantum Physics · Physics 2017-07-19 Glen Evenbly

We review current progress in the functional renormalization group treatment of disordered systems. After an elementary introduction into the phenomenology, we show why in the context of disordered systems a functional renormalization group…

Condensed Matter · Physics 2007-05-23 Kay Joerg Wiese

The renormalization group (RG) is a class of theoretical techniques used to explain the collective physics of interacting, many-body systems. It has been suggested that the RG formalism may be useful in finding and interpreting emergent…

Statistical Mechanics · Physics 2022-03-23 Adam G. Kline , Stephanie E. Palmer

Based on a recently established formalism (U. Ebert, J. Stat. Phys. 82, 183 (1996)) we analyze the diffusive motion of a long polymer in a quenched random medium. The medium is modeled by a frozen semidilute polymer system. In the framework…

Statistical Mechanics · Physics 2015-06-25 Stefan Mueller

This paper presents the construction of novel stabilized finite element methods in the convective-diffusive context that exhibit correct-energy behavior. Classical stabilized formulations can create unwanted artificial energy. Our…

Numerical Analysis · Mathematics 2018-02-06 M. ten Eikelder , I. Akkerman

Complex systems with many degrees of freedom are typically intractable, but some of their behaviors may admit simpler effective descriptions. The question of when such effective descriptions are possible remains open. The paradigmatic…

Statistical Mechanics · Physics 2020-11-26 Charlotte Strandkvist , Pavel Chvykov , Mikhail Tikhonov

We consider the long-ranged Ising spin-glass with random couplings decaying as a power-law of the distance, in the region of parameters where the spin-glass phase exists with a positive droplet exponent. For the Metropolis single-spin-flip…

Disordered Systems and Neural Networks · Physics 2014-08-22 Cecile Monthus

Renormalization is often described as the removal or "integrating out" of high energy degrees of freedom. In the context of quantum matter, one might suspect that quantum entanglement provides a sharp way to characterize such a loss of…

Statistical Mechanics · Physics 2015-06-16 Brian Swingle

A perturbative description of Large Scale Structure is a cornerstone of our understanding of the observed distribution of matter in the universe. Renormalization is an essential and defining step to make this description physical and…

High Energy Physics - Theory · Physics 2016-06-08 Ali Akbar Abolhasani , Mehrdad Mirbabayi , Enrico Pajer

In this work, we develop a new hydrostatic reconstruction procedure to construct well-balanced schemes for one and multilayer shallow water flows, including wetting and drying. Initially, we derive the method for a path-conservative finite…

Numerical Analysis · Mathematics 2024-06-21 Patrick Ersing , Sven Goldberg , Andrew R. Winters

The renormalization group equations for large-scale structure (RG-LSS) describe how the bias and stochastic (noise) parameters -- both of matter and biased tracers such as galaxies -- evolve as a function of the cutoff $\Lambda$ of the…

Cosmology and Nongalactic Astrophysics · Physics 2024-10-10 Henrique Rubira , Fabian Schmidt

The Similarity Renormalization Group (SRG) is a continuous series of unitary transformations that can be implemented as a flow equation. When the relative kinetic energy ($\Trel$) is used in the SRG generator, nuclear structure calculations…

Nuclear Theory · Physics 2011-03-22 K. A. Wendt , R. J. Furnstahl , R. J. Perry

This paper presents a generalization of our earlier work in [19]. In this paper, the two concepts, generic regular decomposition (GRD) and regular-decomposition-unstable (RDU) variety introduced in [19] for generic zero-dimensional systems,…

Symbolic Computation · Computer Science 2013-01-18 Zhenghong Chen , Xiaoxian Tang , Bican Xia