Example of Journal of Nonlinear Science format
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Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format
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Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format Example of Journal of Nonlinear Science format
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open access Open Access

Journal of Nonlinear Science — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Applied Mathematics #73 of 548 up up by 8 ranks
Engineering (all) #52 of 297 down down by 5 ranks
Modeling and Simulation #59 of 290 -
journal-quality-icon Journal quality:
High
calendar-icon Last 4 years overview: 316 Published Papers | 1352 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 17/06/2020
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High
CiteRatio: 5.3
SJR: 1.515
SNIP: 0.684

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

2.104

4% from 2018

Impact factor for Journal of Nonlinear Science from 2016 - 2019
Year Value
2019 2.104
2018 2.017
2017 2.108
2016 1.904
graph view Graph view
table view Table view

4.3

30% from 2019

CiteRatio for Journal of Nonlinear Science from 2016 - 2020
Year Value
2020 4.3
2019 3.3
2018 3.5
2017 3.2
2016 3.5
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 4% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 30% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.624

33% from 2019

SJR for Journal of Nonlinear Science from 2016 - 2020
Year Value
2020 1.624
2019 1.217
2018 1.127
2017 1.568
2016 1.499
graph view Graph view
table view Table view

1.688

25% from 2019

SNIP for Journal of Nonlinear Science from 2016 - 2020
Year Value
2020 1.688
2019 1.348
2018 1.374
2017 1.193
2016 1.318
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has increased by 33% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 25% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

Journal of Nonlinear Science

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Springer

Journal of Nonlinear Science

The mission of the Journal of Nonlinear Science is to publish papers that augment the fundamental ways we describe, model, and predict nonlinear phenomena. Papers should make an original contribution to at least one technical area and should in addition illuminate issues beyon...... Read More

Engineering

i
Last updated on
16 Jun 2020
i
ISSN
0938-8974
i
Impact Factor
High - 1.807
i
Open Access
No
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
SPBASIC
i
Citation Type
Author Year
(Blonder et al, 1982)
i
Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1007/S00332-015-9258-5
A Data-Driven Approximation of the Koopman Operator: Extending Dynamic Mode Decomposition
Matthew O. Williams1, Ioannis G. Kevrekidis1, Clarence W. Rowley1

Abstract:

The Koopman operator is a linear but infinite-dimensional operator that governs the evolution of scalar observables defined on the state space of an autonomous dynamical system and is a powerful tool for the analysis and decomposition of nonlinear dynamical systems In this manuscript, we present a data-driven method for appro... The Koopman operator is a linear but infinite-dimensional operator that governs the evolution of scalar observables defined on the state space of an autonomous dynamical system and is a powerful tool for the analysis and decomposition of nonlinear dynamical systems In this manuscript, we present a data-driven method for approximating the leading eigenvalues, eigenfunctions, and modes of the Koopman operator The method requires a data set of snapshot pairs and a dictionary of scalar observables, but does not require explicit governing equations or interaction with a “black box” integrator We will show that this approach is, in effect, an extension of dynamic mode decomposition (DMD), which has been used to approximate the Koopman eigenvalues and modes Furthermore, if the data provided to the method are generated by a Markov process instead of a deterministic dynamical system, the algorithm approximates the eigenfunctions of the Kolmogorov backward equation, which could be considered as the “stochastic Koopman operator” (Mezic in Nonlinear Dynamics 41(1–3): 309–325, 2005) Finally, four illustrative examples are presented: two that highlight the quantitative performance of the method when presented with either deterministic or stochastic data and two that show potential applications of the Koopman eigenfunctions read more read less

Topics:

Composition operator (58%)58% related to the paper, Dynamic mode decomposition (58%)58% related to the paper, Eigenvalues and eigenvectors (54%)54% related to the paper, Eigenfunction (53%)53% related to the paper, Nonlinear system (51%)51% related to the paper
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1,146 Citations
Journal Article DOI: 10.1007/S00332-012-9130-9
Variants of Dynamic Mode Decomposition: Boundary Condition, Koopman, and Fourier Analyses
Kevin Chen1, Jonathan H. Tu1, Clarence W. Rowley1

Abstract:

Dynamic mode decomposition (DMD) is an Arnoldi-like method based on the Koopman operator. It analyzes empirical data, typically generated by nonlinear dynamics, and computes eigenvalues and eigenmodes of an approximate linear model. Without explicit knowledge of the dynamical operator, it extracts frequencies, growth rates, a... Dynamic mode decomposition (DMD) is an Arnoldi-like method based on the Koopman operator. It analyzes empirical data, typically generated by nonlinear dynamics, and computes eigenvalues and eigenmodes of an approximate linear model. Without explicit knowledge of the dynamical operator, it extracts frequencies, growth rates, and spatial structures for each mode. We show that expansion in DMD modes is unique under certain conditions. When constructing mode-based reduced-order models of partial differential equations, subtracting a mean from the data set is typically necessary to satisfy boundary conditions. Subtracting the mean of the data exactly reduces DMD to the temporal discrete Fourier transform (DFT); this is restrictive and generally undesirable. On the other hand, subtracting an equilibrium point generally preserves the DMD spectrum and modes. Next, we introduce an “optimized” DMD that computes an arbitrary number of dynamical modes from a data set. Compared to DMD, optimized DMD is superior at calculating physically relevant frequencies, and is less numerically sensitive. We test these decomposition methods on data from a two-dimensional cylinder fluid flow at a Reynolds number of 60. Time-varying modes computed from the DMD variants yield low projection errors. read more read less

Topics:

Dynamic mode decomposition (72%)72% related to the paper, Boundary value problem (52%)52% related to the paper, Discrete Fourier transform (52%)52% related to the paper, Fourier transform (51%)51% related to the paper, Partial differential equation (50%)50% related to the paper
View PDF
672 Citations
Journal Article DOI: 10.1007/BF02429870
Coupled Nonlinear Oscillators and the Symmetries of Animal Gaits
James J. Collins1, James J. Collins2, Ian Stewart3

Abstract:

Animal locomotion typically employs several distinct periodic patterns of leg movements, known as gaits. It has long been observed that most gaits possess a degree of symmetry. Our aim is to draw attention to some remarkable parallels between the generalities of coupled nonlinear oscillators and the observed symmetries of gai... Animal locomotion typically employs several distinct periodic patterns of leg movements, known as gaits. It has long been observed that most gaits possess a degree of symmetry. Our aim is to draw attention to some remarkable parallels between the generalities of coupled nonlinear oscillators and the observed symmetries of gaits, and to describe how this observation might impose constraints on the general structure of the neural circuits, i.e. central pattern generators, that control locomotion. We compare the symmetries of gaits with the symmetry-breaking oscillation patterns that should be expected in various networks of symmetrically coupled nonlinear oscillators. We discuss the possibility that transitions between gaits may be modeled as symmetry-breaking bifurcations of such oscillator networks. The emphasis is on general model-independent features of such networks, rather than on specific models. Each type of network generates a characteristic set of gait symmetries, so our results may be interpreted as an analysis of the general structure required of a central pattern generator in order to produce the types of gait observed in the natural world. The approach leads to natural hierarchies of gaits, ordered by symmetry, and to natural sequences of gait bifurcations. We briefly discuss how the ideas could be extended to hexapodal gaits. read more read less

Topics:

Gait (59%)59% related to the paper
501 Citations
Journal Article DOI: 10.1007/S00332-002-0466-4
Boussinesq Equations and Other Systems for Small-Amplitude Long Waves in Nonlinear Dispersive Media. I: Derivation and Linear Theory
Bona1, Chen2, Saut3

Abstract:

Considered herein are a number of variants of the classical Boussinesq system and their higher-order generalizations Such equations were first derived by Boussinesq to describe the two-way propagation of small-amplitude, long wavelength, gravity waves on the surface of water in a canal These systems arise also when modeling t... Considered herein are a number of variants of the classical Boussinesq system and their higher-order generalizations Such equations were first derived by Boussinesq to describe the two-way propagation of small-amplitude, long wavelength, gravity waves on the surface of water in a canal These systems arise also when modeling the propagation of long-crested waves on large lakes or the ocean and in other contexts Depending on the modeling of dispersion, the resulting system may or may not have a linearization about the rest state which is well posed Even when well posed, the linearized system may exhibit a lack of conservation of energy that is at odds with its status as an approximation to the Euler equations In the present script, we derive a four-parameter family of Boussinesq systems from the two-dimensional Euler equations for free-surface flow and formulate criteria to help decide which of these equations one might choose in a given modeling situation The analysis of the systems according to these criteria is initiated read more read less

Topics:

Boussinesq approximation (water waves) (72%)72% related to the paper, Euler equations (62%)62% related to the paper, Nonlinear system (57%)57% related to the paper, Linearization (56%)56% related to the paper, Dispersion (water waves) (54%)54% related to the paper
494 Citations
Journal Article DOI: 10.1007/BF02440162
Time Integration and Discrete Hamiltonian Systems
Oscar Gonzalez1

Abstract:

This paper develops a formalism for the design of conserving time-integration schemes for Hamiltonian systems with symmetry. The main result is that, through the introduction of a discrete directional derivative, implicit second-order conserving schemes can be constructed for general systems which preserve the Hamiltonian alo... This paper develops a formalism for the design of conserving time-integration schemes for Hamiltonian systems with symmetry. The main result is that, through the introduction of a discrete directional derivative, implicit second-order conserving schemes can be constructed for general systems which preserve the Hamiltonian along with a certain class of other first integrals arising from affine symmetries. Discrete Hamiltonian systems are introduced as formal abstractions of conserving schemes and are analyzed within the context of discrete dynamical systems; in particular, various symmetry and stability properties are investigated. read more read less

Topics:

Covariant Hamiltonian field theory (63%)63% related to the paper, Hamiltonian system (62%)62% related to the paper, Discrete system (62%)62% related to the paper, Superintegrable Hamiltonian system (60%)60% related to the paper, Hamiltonian path problem (58%)58% related to the paper
462 Citations
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Frequently asked questions

1. Can I write Journal of Nonlinear Science in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the Journal of Nonlinear Science guidelines and auto format it.

2. Do you follow the Journal of Nonlinear Science guidelines?

Yes, the template is compliant with the Journal of Nonlinear Science guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in Journal of Nonlinear Science?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the Journal of Nonlinear Science citation style.

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Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for Journal of Nonlinear Science.

5. Can I use a manuscript in Journal of Nonlinear Science that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper Journal of Nonlinear Science that you can download at the end.

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7. Where can I find the template for the Journal of Nonlinear Science?

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Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

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SciSpace's Journal of Nonlinear Science is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

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After writing your paper autoformatting in Journal of Nonlinear Science, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is Journal of Nonlinear Science's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for Journal of Nonlinear Science?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Nonlinear Science. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Nonlinear Science?

The 5 most common citation types in order of usage for Journal of Nonlinear Science are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the Journal of Nonlinear Science?

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16. Can I download Journal of Nonlinear Science in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in Journal of Nonlinear Science Endnote style according to Elsevier guidelines.

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