journal of statistical mechanics theory and experiment quartile

Journal of Statistical Mechanics: Theory and Experiment

Subject Area and Category

• Statistics, Probability and Uncertainty
• Statistics and Probability
• Statistical and Nonlinear Physics

IOP Publishing Ltd.

Publication type

Information.

How to publish in this journal

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The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values.

The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. It is based on the idea that 'all citations are not created equal'. SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.

Evolution of the number of published documents. All types of documents are considered, including citable and non citable documents.

This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric.

Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal.

Evolution of the number of total citation per document and external citation per document (i.e. journal self-citations removed) received by a journal's published documents during the three previous years. External citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents.

International Collaboration accounts for the articles that have been produced by researchers from several countries. The chart shows the ratio of a journal's documents signed by researchers from more than one country; that is including more than one country address.

Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. those documents other than research articles, reviews and conference papers.

Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those not cited during the following year.

Evolution of the percentage of female authors.

Evolution of the number of documents cited by public policy documents according to Overton database.

Evolution of the number of documents related to Sustainable Development Goals defined by United Nations. Available from 2018 onwards.

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Journal Of Statistical Mechanics: Theory And Experiment impact factor, indexing, ranking (2024)

Aim and Scope

The Journal Of Statistical Mechanics: Theory And Experiment is a research journal that publishes research related to Decision Sciences; Mathematics; Physics and Astronomy . This journal is published by the IOP Publishing Ltd.. The ISSN of this journal is 17425468 . Based on the Scopus data, the SCImago Journal Rank (SJR) of journal of statistical mechanics: theory and experiment is 0.527 .

Journal Of Statistical Mechanics: Theory And Experiment Ranking

The SJR (SCImago Journal Rank) measures citations weighted by prestige. It is useful for comparing journals within the same field, and forms the basis of the subject category ranking. A journal SJR indicator is a numeric value representing the average number of weighted citations received during a selected year per document published in that journal during the previous three years, as indexed by Scopus. Higher SJR indicator values are meant to indicate greater journal prestige. SJR is developed by the Scimago Lab, originated from a research group at University of Granada. Q1 journals are cited more often and by more prestigious journals than those in the other quartiles.

Each subject category of journals is divided into four quartiles: Q1, Q2, Q3, Q4. Q1 is occupied by the top 25% of journals in the list; Q2 is occupied by journals in the 25 to 50% group; Q3 is occupied by journals in the 50 to 75% group and Q4 is occupied by journals in the 75 to 100% group.

CiteScore of an academic journal is a measure reflecting the yearly average number of citations to recent articles published in that journal. This journal evaluation metric was launched in December 2016 by Elsevier as an alternative to the generally used JCR impact factors (calculated by Clarivate). CiteScore is based on the citations recorded in the Scopus database rather than in JCR, and those citations are collected for articles published in the preceding four years instead of two or five.

Source Normalized Impact per Paper (SNIP) is calculated annually from Scopus data. It is a sophisticated metric that intrinsically accounts for field-specific differences in citation practices.

Important Metrics

The journal of statistical mechanics: theory and experiment is indexed in:

An indexed journal means that the journal has gone through and passed a review process of certain requirements done by a journal indexer.

The Web of Science Core Collection includes the Science Citation Index Expanded (SCIE), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (AHCI), and Emerging Sources Citation Index (ESCI).

The latest Quartile of journal of statistical mechanics: theory and experiment is Q2 .

Journal Publication Time

The publication time may vary depending on factors such as the complexity of the research and the current workload of the editorial team. Journals typically request reviewers to submit their reviews within 3-4 weeks. However, some journals lack mechanisms to enforce this deadline, making it difficult to predict the duration of the peer review process.

The review time also depends upon the quality of the research paper.

Call for Papers

Visit to the official website of the journal/ conference to check the details about call for papers.

How to publish in Journal Of Statistical Mechanics: Theory And Experiment?

If your research is related to Decision Sciences; Mathematics; Physics and Astronomy, then visit the official website of journal of statistical mechanics: theory and experiment and send your manuscript.

Tips for publishing in Journal Of Statistical Mechanics: Theory And Experiment:

• Selection of research problem.
• Presenting a solution.
• Designing the paper.
• Make your manuscript publication worthy.
• Write an effective results section.
• Mind your references.

Acceptance Rate

Final summary.

• It is published by IOP Publishing Ltd. .
• The journal is indexed in UGC CARE, Scopus .
• The (SJR) SCImago Journal Rank is 0.527 .

SIMILIAR JOURNALS

MODERN PHYSICS LETTERS A

CLASSICAL AND QUANTUM GRAVITY

GENERAL RELATIVITY AND GRAVITATION

PHYSICS LETTERS B

PHYSICA D-NONLINEAR PHENOMENA

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION

SCIENTIA SINICA-PHYSICA MECHANICA & ASTRONOMICA

ASTROPARTICLE PHYSICS

COMPTES RENDUS PHYSIQUE

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Journal of Statistical Mechanics: Theory and Experiment

The International School for Advanced Studies (SISSA) was founded in 1978 and was the first institution in Italy to promote post-graduate courses leading to a Doctor Philosophiae (or PhD) degree. A centre of excellence among Italian and international universities, the school has around 65 teachers, 100 post docs and 245 PhD students, and is located in Trieste, in a campus of more than 10 hectares with wonderful views over the Gulf of Trieste.

SISSA hosts a very high-ranking, large and multidisciplinary scientific research output. The scientific papers produced by its researchers are published in high impact factor, well-known international journals, and in many cases in the world's most prestigious scientific journals such as Nature and Science. Over 900 students have so far started their careers in the field of mathematics, physics and neuroscience research at SISSA.

Journal of Statistical Mechanics: Theory and Experiment (JSTAT) is a multi-disciplinary, peer-reviewed international journal created by the International School for Advanced Studies (SISSA) and IOP Publishing (IOP).  JSTAT covers all aspects of statistical physics, including experimental work that impacts on the subject.

Open all abstracts , in this tab

Anna Dawid and Yann LeCun J. Stat. Mech. (2024) 104011

Current automated systems have crucial limitations that need to be addressed before artificial intelligence can reach human-like levels and bring new technological revolutions. Among others, our societies still lack level-5 self-driving cars, domestic robots, and virtual assistants that learn reliable world models, reason, and plan complex action sequences. In these notes, we summarize the main ideas behind the architecture of autonomous intelligence of the future proposed by Yann LeCun. In particular, we introduce energy-based and latent variable models and combine their advantages in the building block of LeCun's proposal, that is, in the hierarchical joint-embedding predictive architecture.

Florent Krzakala and Lenka Zdeborová J. Stat. Mech. (2024) 101001

Preetum Nakkiran et al J. Stat. Mech. (2021) 124003

We show that a variety of modern deep learning tasks exhibit a 'double-descent' phenomenon where, as we increase model size, performance first gets worse and then gets better. Moreover, we show that double descent occurs not just as a function of model size, but also as a function of the number of training epochs. We unify the above phenomena by defining a new complexity measure we call the effective model complexity and conjecture a generalized double descent with respect to this measure. Furthermore, our notion of model complexity allows us to identify certain regimes where increasing (even quadrupling) the number of train samples actually hurts test performance.

Boaz Barak et al J. Stat. Mech. (2024) 104008

This manuscript is the lecture notes of B. Barak's course in the Les Houches 'Statistical Physics and Machine Learning' summer school in 2022. It surveys various proxies for computational hardness in random planted problems, from the low-degree likelihood ratio to statistical query complexity and the Franz–Parisi criterion, as well as the various relationships between those criteria. We also present a few aspects of the study of deep learning, from both a theoretical and empirical point of view.

Vincent D Blondel et al J. Stat. Mech. (2008) P10008

We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.

Erin Grant et al J. Stat. Mech. (2024) 104005

Vincent Blondel et al J. Stat. Mech. (2024) 10R001

The Louvain method was proposed 15 years ago as a heuristic method for the fast detection of communities in large networks. During this period, it has emerged as one of the most popular methods for community detection: the task of partitioning vertices of a network into dense groups, usually called communities or clusters. Here, after a short introduction to the method, we give an overview of the different generalizations, modifications and improvements that have been proposed in the literature, and also survey the quality functions, beyond modularity, for which it has been implemented. Finally, we conclude with a discussion on the limitations of the method and perspectives for future research.

Tuan Minh Pham and Kunihiko Kaneko J. Stat. Mech. (2024) 113501

The study of adaptive dynamics, involving many degrees of freedom on two separated timescales, one for fast changes of state variables and another for the slow adaptation of parameters controlling the former's dynamics is crucial for understanding feedback mechanisms underlying evolution and learning. We present a path-integral approach à la Martin–Siggia–Rose-De Dominicis–Janssen to analyse non-equilibrium phase transitions in such dynamical systems. As an illustration, we apply our framework to the adaptation of gene-regulatory networks under a dynamic genotype-phenotype map: phenotypic variations are shaped by the fast stochastic gene-expression dynamics and are coupled to the slowly evolving distribution of genotypes, each encoded by a network structure. We establish that under this map, genotypes corresponding to reciprocal networks of coherent feedback loops are selected within an intermediate range of environmental noise, leading to phenotypic robustness.

C Lauditi et al J. Stat. Mech. (2024) 104001

In recent years statistical physics has proven to be a valuable tool to probe into large dimensional inference problems such as the ones occurring in machine learning. Statistical physics provides analytical tools to study fundamental limitations in their solutions and proposes algorithms to solve individual instances. In these notes, based on the lectures by Marc Mézard in 2022 at the summer school in Les Houches, we will present a general framework that can be used in a large variety of problems with weak long-range interactions, including the compressed sensing problem, or the problem of learning in a perceptron. We shall see how these problems can be studied at the replica symmetric level, using developments of the cavity methods, both as a theoretical tool and as an algorithm.

Francis Bach et al J. Stat. Mech. (2024) 104010

The sum-of-squares (SOS) approximation method is a technique used in optimization problems to derive lower bounds on the optimal value of an objective function. By representing the objective function as a sum of squares in a feature space, the SOS method transforms non-convex global optimization problems into solvable semidefinite programs. This note presents an overview of the SOS method. We start with its application in finite-dimensional feature spaces and, subsequently, we extend it to infinite-dimensional feature spaces using reproducing kernels (k-SOS). Additionally, we highlight the utilization of SOS for estimating some relevant quantities in information theory, including the log-partition function.

Latest articles

Andrea Muratori and Guilhem Semerjian J. Stat. Mech. (2024) 113405

Zakhar Kabluchko and Matthias Löwe J. Stat. Mech. (2024) 113206

We prove quenched propagation of chaos in the Random field mean-field Ising model, also known ad the Random field Curie–Weiss model. We show that in the paramagnetic phase, i.e. in the regime where temperature and distribution of the external field admit a unique minimizer of the expected Helmholtz free energy, quenched propagation of chaos holds. By the latter we mean that the finite-dimensional marginals of the Gibbs measure converge towards a product measure with the correct expectation as the system size goes to infinity. This holds independently of whether the system is in a high-temperature phase or at a phase transition point and alsmost surely with respect to the random external field. If the Helmholtz free energy possesses several minima, there are several possible equilibrium measures. In this case, we show that the system picks one of them at random (depending on the realization of the random external field) and propagation of chaos with respect to a product measure with the same marginals as the one randomly picked holds true. We illustrate our findings in a simple example.

Stefano Sarao Mannelli et al J. Stat. Mech. (2024) 114001

A wide range of empirical and theoretical works have shown that overparameterisation can amplify the performance of neural networks. According to the lottery ticket hypothesis , overparameterised networks have an increased chance of containing a sub-network that is well-initialised to solve the task at hand. A more parsimonious approach, inspired by animal learning, consists in guiding the learner towards solving the task by curating the order of the examples, i.e.  providing a curriculum . However, this learning strategy seems to be hardly beneficial in deep learning applications. In this work, we undertake an analytical study that connects curriculum learning and overparameterisation. In particular, we investigate their interplay in the online learning setting for a 2-layer network in the XOR-like Gaussian Mixture problem. Our results show that a high degree of overparameterisation—while simplifying the problem—can limit the benefit from curricula, providing a theoretical account of the ineffectiveness of curricula in deep learning.

Jacopo Niedda et al J. Stat. Mech. (2024) 113301

In this paper, we investigate the marginally stable nature of the low-temperature trivial spin-glass phase in spherical p  = 2 spin glass by perturbing the system with three different kinds of non-linear interactions. In particular, we compare the effect of three additional dense four-body interactions, namely ferromagnetic couplings, purely disordered couplings and couplings with competing disordered and ferromagnetic interactions. Our study, characterized by the effort to present in a clear and pedagogical way the derivation of all the results, shows that the marginal stability property of the spherical spin glass depends in fact on which kind of perturbation is applied to the system. In general, a certain degree of frustration is needed in the additional terms in order to induce a transition from a trivial to a non-trivial spin-glass phase. On the other hand, the addition of generic non-frustrated interactions does not destabilize the trivial spin-glass phase.

Hsin-Lun Li J. Stat. Mech. (2024) 113404

The mixed Hegselmann–Krause (HK) model covers the synchronous HK model, the asynchronous HK model and the Deffuant model. Previous studies (Li 2022 Discrete Continuous Dyn. Syst. B 27 1149–62, Li 2023 Discrete Continuous Dyn. Syst. B 28 2981–93) deal with the mixed HK model on finite graphs. In this study, we discuss the mixed HK model on infinite graphs which also covers the HK model and the Deffuant model on infinite graphs. Traditionally, the HK and Deffuant models are discussed separately, with the former belonging to the group interaction and the latter to the pair interaction. The mixed HK model interprets both group and pair interactions. We investigate the conditions under which asymptotic stability holds or under which any two vertices in the same component approach each other after some finite time in this dual interaction model.

Review articles

Annabel L Davies and Tobias Galla J. Stat. Mech. (2022) 11R001

Network meta-analysis (NMA) is a technique used in medical statistics to combine evidence from multiple medical trials. NMA defines an inference and information processing problem on a network of treatment options and trials connecting the treatments. We believe that statistical physics can offer useful ideas and tools for this area, including from the theory of complex networks, stochastic modelling and simulation techniques. The lack of a unique source that would allow physicists to learn about NMA effectively is a barrier to this. In this article we aim to present the 'NMA problem' and existing approaches to it coherently and in a language accessible to statistical physicists. We also summarise existing points of contact between statistical physics and NMA, and describe our ideas of how physics might make a difference for NMA in the future. The overall goal of the article is to attract physicists to this interesting, timely and worthwhile field of research.

Shamik Gupta et al J. Stat. Mech. (2014) R08001

The phenomenon of spontaneous synchronization, particularly within the framework of the Kuramoto model, has been a subject of intense research over the years. The model comprises oscillators with distributed natural frequencies interacting through a mean-field coupling, and serves as a paradigm to study synchronization. In this review, we put forward a general framework in which we discuss in a unified way known results with more recent developments obtained for a generalized Kuramoto model that includes inertial effects and noise. We describe the model from a different perspective, highlighting the long-range nature of the interaction between the oscillators, and emphasizing the equilibrium and out-of-equilibrium aspects of its dynamics from a statistical physics point of view. In this review, we first introduce the model and discuss both for the noiseless and noisy dynamics and for unimodal frequency distributions the synchronization transition that occurs in the stationary state. We then introduce the generalized model, and analyze its dynamics using tools from statistical mechanics. In particular, we discuss its synchronization phase diagram for unimodal frequency distributions. Next, we describe deviations from the mean-field setting of the Kuramoto model. To this end, we consider the generalized Kuramoto dynamics on a one-dimensional periodic lattice on the sites of which the oscillators reside and interact with one another with a coupling that decays as an inverse power-law of their separation along the lattice. For two specific cases, namely, in the absence of noise and inertia, and in the case when the natural frequencies are the same for all the oscillators, we discuss how the long-time transition to synchrony is governed by the dynamics of the mean-field mode (zero Fourier mode) of the spatial distribution of the oscillator phases.

Open access

Eldad Bettelheim and Baruch Meerson J. Stat. Mech. (2024) 113204

Konstantinos Chalas et al J. Stat. Mech. (2024) 103101

Local relaxation after a quench in 1D quantum many-body systems is a well-known and very active problem with rich phenomenology. Except in pathological cases, the local relaxation is accompanied by the local restoration of the symmetries broken by the initial state that are preserved by unitary evolution. Recently, the entanglement asymmetry has been introduced as a probe to study the interplay between symmetry breaking and relaxation in an extended quantum system. In particular, using the entanglement asymmetry, it has been shown that the more a symmetry is initially broken, the faster it may be restored. This surprising effect, which has also been observed in trapped-ion experiments, can be seen as a quantum version of the Mpemba effect, and is manifested by the crossing at a finite time of the entanglement asymmetry curves of two different initial symmetry-breaking configurations. In this paper we show that, by tuning the initial state, the symmetry dynamics in free fermionic systems can display much richer behavior than seen previously. In particular, for certain classes of initial states, including the ground states of free fermionic models with long-range couplings, the entanglement asymmetry can exhibit multiple crossings. This illustrates that the existence of the quantum Mpemba effect can only be inferred by examining the late-time behavior of the entanglement asymmetry.

Sarah K Wyse and Eric Foxall J. Stat. Mech. (2024) 113403

The extent to which committed minorities can overturn social conventions is an active area of research in the mathematical modeling of opinion dynamics. Researchers generally use simulations of agent-based models (ABMs) to compute approximate values for the minimum committed minority size needed to overturn a social convention. In this manuscript, we expand on previous work by studying an ABM's mean-field behavior using ordinary differential equation models and a new tool, namely opinion response functions (ORFs). Using this method allows for formal analysis of the deterministic model, which can provide a theoretical explanation for observed behaviors, e.g. coexistence or overturning of opinions. In particular, ORFs are a method for characterizing equilibria in our social model. Our analysis confirms earlier numerical results and supplements them with a precise formula for computing the minimum committed minority size required to overturn a social convention.

Gabrielle Blanchet et al J. Stat. Mech. (2024) 113101

Aleksejus Kononovicius and Bronislovas Kaulakys J. Stat. Mech. (2024) 113201

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• 2004-present Journal of Statistical Mechanics: Theory and Experiment doi: 10.1088/issn.1742-5468 Online ISSN: 1742-5468

Journal of Statistical Mechanics: Theory and Experiment -Impact Score, Ranking

About journal of statistical mechanics: theory and experiment.

Journal of Statistical Mechanics: Theory and Experiment is a reputed research journal publish the research in the field/area related to Statistics and Probability (Q2); Statistical and Nonlinear Physics (Q3); Statistics, Probability and Uncertainty (Q3) . It is published by IOP Publishing Ltd. . The journal has an h-index of 86. The overall rank of this journal is 9861 . The more details like ISSN, Journal Quartile, SJR Score, ISSN, and other important details are provided in the following section.

Important Metrics

Journal of statistical mechanics: theory and experiment impact score 2024.

The latest impact score of Journal of Statistical Mechanics: Theory and Experiment is 1.46.

Credit & Source: Scopus.

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Journal of Statistical Mechanics-Theory and Experiment

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Impact Factor : 2.200 (based on Web of Science 2023)

• # 56 / 131 (Q2) in Mechanics
• # 7 / 50 (Q1) in Physics, Mathematical

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