Example of Inverse Problems in Science and Engineering format
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Example of Inverse Problems in Science and Engineering format Example of Inverse Problems in Science and Engineering format Example of Inverse Problems in Science and Engineering format Example of Inverse Problems in Science and Engineering format Example of Inverse Problems in Science and Engineering format Example of Inverse Problems in Science and Engineering format Example of Inverse Problems in Science and Engineering format Example of Inverse Problems in Science and Engineering format Example of Inverse Problems in Science and Engineering format Example of Inverse Problems in Science and Engineering format Example of Inverse Problems in Science and Engineering format Example of Inverse Problems in Science and Engineering format
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open access Open Access

Inverse Problems in Science and Engineering — Template for authors

Publisher: Taylor and Francis
Categories Rank Trend in last 3 yrs
Engineering (all) #96 of 297 down down by 21 ranks
Applied Mathematics #185 of 548 down down by 15 ranks
Computer Science Applications #324 of 693 down down by 48 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 336 Published Papers | 909 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 19/07/2020
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Related Journals

open access Open Access
recommended Recommended

Taylor and Francis

Quality:  
High
CiteRatio: 6.8
SJR: 1.321
SNIP: 1.764
open access Open Access

Taylor and Francis

Quality:  
High
CiteRatio: 4.6
SJR: 0.601
SNIP: 1.294
open access Open Access

Springer

Quality:  
High
CiteRatio: 3.4
SJR: 0.291
SNIP: 0.951
open access Open Access
recommended Recommended

IEEE

Quality:  
High
CiteRatio: 15.2
SJR: 2.01
SNIP: 2.43

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.

1.314

10% from 2018

Impact factor for Inverse Problems in Science and Engineering from 2016 - 2019
Year Value
2019 1.314
2018 1.464
2017 1.125
2016 1.033
graph view Graph view
table view Table view

2.7

CiteRatio for Inverse Problems in Science and Engineering from 2016 - 2020
Year Value
2020 2.7
2019 2.7
2018 2.3
2017 2.1
2016 2.0
graph view Graph view
table view Table view

insights Insights

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

insights Insights

  • 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.

0.426

11% from 2019

SJR for Inverse Problems in Science and Engineering from 2016 - 2020
Year Value
2020 0.426
2019 0.481
2018 0.506
2017 0.406
2016 0.456
graph view Graph view
table view Table view

0.993

13% from 2019

SNIP for Inverse Problems in Science and Engineering from 2016 - 2020
Year Value
2020 0.993
2019 1.146
2018 0.93
2017 0.958
2016 0.912
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 11% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has decreased by 13% in last years.
  • This journal’s SNIP is in the top 10 percentile category.
Inverse Problems in Science and Engineering

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Taylor and Francis

Inverse Problems in Science and Engineering

When it is possible to determine governing equation(s), shape(s) and size(s) of the domain(s), boundary and initial conditions, material properties of the media contained in the field, and internal sources and external forces or inputs, then the analysis determining the unknow...... Read More

Engineering

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Last updated on
19 Jul 2020
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ISSN
1741-5977
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Impact Factor
High - 1.048
i
Open Access
No
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Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
i
Endnote Style
Download Available
i
Bibliography Name
Taylor and Francis Custom Citation
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Citation Type
Numbered
[25]
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Bibliography Example
Blonder GE, Tinkham M, Klapwijk TM. Transition from metallic to tunneling regimes in superconducting microconstrictions: Excess current, charge imbalance, and supercurrent conversion. Phys Rev B. 1982; 25(7):4515–4532. Available from: 10.1103/PhysRevB.25.4515.

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1080/17415977.2018.1518444
Deep learning for photoacoustic tomography from sparse data.
Stephan Antholzer1, Markus Haltmeier1, Johannes Schwab1

Abstract:

The development of fast and accurate image reconstruction algorithms is a central aspect of computed tomography. In this paper, we investigate this issue for the sparse data problem in photoacousti... The development of fast and accurate image reconstruction algorithms is a central aspect of computed tomography. In this paper, we investigate this issue for the sparse data problem in photoacousti... read more read less

Topics:

Iterative reconstruction (64%)64% related to the paper, Deep learning (53%)53% related to the paper, Inverse problem (50%)50% related to the paper
View PDF
210 Citations
Journal Article DOI: 10.1080/17415977.2015.1017485
Detection of material interfaces using a regularized level set method in piezoelectric structures
S.S. Nanthakumar1, Tom Lahmer1, Xiaoying Zhuang2, Goangseup Zi3, Timon Rabczuk1

Abstract:

An algorithm to solve the inverse problem of detecting inclusion interfaces in a piezoelectric structure is proposed. The material interfaces are implicitly represented by level sets which are identified by applying regularization using total variation penalty terms. The inverse problem is solved iteratively and the extended ... An algorithm to solve the inverse problem of detecting inclusion interfaces in a piezoelectric structure is proposed. The material interfaces are implicitly represented by level sets which are identified by applying regularization using total variation penalty terms. The inverse problem is solved iteratively and the extended finite element method is used for the analysis of the structure in each iteration. The formulation is presented for three-dimensional structures and inclusions made of different materials are detected by using multiple level sets. The results obtained prove that the iterative procedure proposed can determine the location and approximate shape of material sub-domains in the presence of higher noise levels. read more read less

Topics:

Level set method (57%)57% related to the paper, Inverse problem (56%)56% related to the paper, Extended finite element method (53%)53% related to the paper, Regularization (mathematics) (51%)51% related to the paper
View PDF
205 Citations
Journal Article DOI: 10.1080/17415977.2011.551830
A survey of applications of the MFS to inverse problems
Andreas Karageorghis1, Daniel Lesnic2, Liviu Marin3

Abstract:

The method of fundamental solutions (MFS) is a relatively new method for the numerical solution of boundary value problems and initial/boundary value problems governed by certain partial differential equations. The ease with which it can be implemented and its effectiveness have made it a very popular tool for the solution of... The method of fundamental solutions (MFS) is a relatively new method for the numerical solution of boundary value problems and initial/boundary value problems governed by certain partial differential equations. The ease with which it can be implemented and its effectiveness have made it a very popular tool for the solution of a large variety of problems arising in science and engineering. In recent years, it has been used extensively for a particular class of such problems, namely inverse problems. In this study, in view of the growing interest in this area, we review the applications of the MFS to inverse and related problems, over the last decade. read more read less

Topics:

Method of fundamental solutions (67%)67% related to the paper, Singular boundary method (57%)57% related to the paper, Boundary value problem (53%)53% related to the paper
202 Citations
Journal Article DOI: 10.1080/17415977.2010.492509
Iterative thresholding compressed sensing MRI based on contourlet transform
Xiaobo Qu1, Weiru Zhang1, Di Guo1, Congbo Cai1, Shuhui Cai1, Zhong Chen1

Abstract:

Reducing the acquisition time is important for clinical magnetic resonance imaging (MRI). Compressed sensing has recently emerged as a theoretical foundation for the reconstruction of magnetic resonance images from undersampled k-space measurements, assuming those images are sparse in a certain transform domain. However, most... Reducing the acquisition time is important for clinical magnetic resonance imaging (MRI). Compressed sensing has recently emerged as a theoretical foundation for the reconstruction of magnetic resonance images from undersampled k-space measurements, assuming those images are sparse in a certain transform domain. However, most real-world signals are compressible rather than exactly sparse. For example, the commonly used two-dimensional wavelet for compressed sensing MRI (CS-MRI) does not sparsely represent curves and edges. In this article, we introduce a geometric image transform, the contourlet, to overcome this shortage. In addition, the improved redundancy provided by the contourlet can successfully suppress the pseudo-Gibbs phenomenon, a tiresome artefact produced by undersampling of k-space, around the singularities of images. For numerical calculation, a simple but effective iterative thresholding algorithm is employed to solve l 1 norm optimization for CS-MRI. Considering the recovered information ... read more read less

Topics:

Contourlet (62%)62% related to the paper, Compressed sensing (55%)55% related to the paper, Wavelet (54%)54% related to the paper, Undersampling (51%)51% related to the paper
156 Citations
Journal Article DOI: 10.1080/17415977.2011.624770
CMARS: a new contribution to nonparametric regression with multivariate adaptive regression splines supported by continuous optimization
Gerhard-Wilhelm Weber1, İnci Batmaz1, Gülser Köksal1, Pakize Taylan1, Fatma Yerlikaya-Özkurt1

Abstract:

Regression analysis is a widely used statistical method for modelling relationships between variables. Multivariate adaptive regression splines (MARS) especially is very useful for high-dimensional problems and fitting nonlinear multivariate functions. A special advantage of MARS lies in its ability to estimate contributions ... Regression analysis is a widely used statistical method for modelling relationships between variables. Multivariate adaptive regression splines (MARS) especially is very useful for high-dimensional problems and fitting nonlinear multivariate functions. A special advantage of MARS lies in its ability to estimate contributions of some basis functions so that both additive and interactive effects of the predictors are allowed to determine the response variable. The MARS method consists of two parts: forward and backward algorithms. Through these algorithms, it seeks to achieve two objectives: a good fit to the data, but a simple model. In this article, we use a penalized residual sum of squares for MARS as a Tikhonov regularization problem, and treat this with continuous optimization technique, in particular, the framework of conic quadratic programming. We call this new approach to MARS as CMARS, and consider it as becoming an important complementary and model-based alternative to the backward stepwise algo... read more read less

Topics:

Multivariate adaptive regression splines (64%)64% related to the paper, Nonparametric regression (61%)61% related to the paper, Regression analysis (55%)55% related to the paper, Residual sum of squares (54%)54% related to the paper, Tikhonov regularization (54%)54% related to the paper
129 Citations
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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 Inverse Problems in Science and Engineering citation style.

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13. What is Sherpa RoMEO Archiving Policy for Inverse Problems in Science and Engineering?

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 Inverse Problems in Science and Engineering. 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 Inverse Problems in Science and Engineering?

The 5 most common citation types in order of usage for Inverse Problems in Science and Engineering are:.

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

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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 Inverse Problems in Science and Engineering Endnote style according to Elsevier guidelines.

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