From c6c5ad539c392c60eeeb03a37b7885c73ef16e61 Mon Sep 17 00:00:00 2001 From: PieterjanR Date: Sat, 27 May 2023 18:02:12 +0200 Subject: [PATCH 1/9] Added first draft of paper --- paper/paper.bib | 139 ++++++++++++++++++++++++++++++++++++++++++++++++ paper/paper.md | 48 +++++++++++++++++ 2 files changed, 187 insertions(+) create mode 100644 paper/paper.bib create mode 100644 paper/paper.md diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 0000000..84a05cd --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,139 @@ +@article{bezanson2017, + title = {Julia: A fresh approach to numerical computing}, + doi = {10.1137/141000671}, + author = {Bezanson, Jeff and Edelman, Alan and Karpinski, Stefan and Shah, Viral B}, + journal = {SIAM review}, + volume = {59}, + number = {1}, + pages = {65--98}, + year = {2017} +} + +@book{bishop2006, + title = {Pattern recognition and machine learning}, + author = {Bishop, Christopher M and Nasrabadi, Nasser M}, + volume = {4}, + number = {4}, + year = {2006} +} + +@article{blondeel2020, + title = {p-refined multilevel quasi-Monte Carlo for Galerkin finite element methods with applications in civil engineering}, + author = {Blondeel, Philippe and Robbe, Pieterjan and Van hoorickx, C{\'e}dric and Fran{\c{c}}ois, Stijn and Lombaert, Geert and Vandewalle, Stefan}, + journal = {Algorithms}, + volume = {13}, + number = {5}, + pages = {110}, + year = {2020} +} + +@article{chiang2000, + title = {Phase information and the evolution of cosmological density perturbations}, + author = {Chiang, Lung-Yih and Coles, Peter}, + journal = {Monthly Notices of the Royal Astronomical Society}, + volume = {311}, + number = {4}, + pages = {809--824}, + year = {2000} +} + +@book{chiles2012, + title = {Geostatistics: modeling spatial uncertainty}, + author = {Chiles, Jean-Paul and Delfiner, Pierre}, + volume = {713}, + year = {2012}, + publisher = {John Wiley \& Sons} +} + +@inproceedings{cui2018, + title = {Uncertainty quantification of electronic and photonic ICs with non-Gaussian correlated process variations}, + author = {Cui, Chunfeng and Zhang, Zheng}, + booktitle = {Proceedings of the International Conference on Computer-Aided Design}, + pages = {1--8}, + year = {2018} +} + +@article{GSTools, + author = {M\"uller, S. and Sch\"uler, L. and Zech, A. and He{\ss}e, F.}, + title = {\texttt{GSTools} v1.3: a toolbox for geostatistical modelling in Python}, + journal = {Geoscientific Model Development}, + volume = {15}, + year = {2022}, + number = {7}, + pages = {3161--3182}, + url = {https://gmd.copernicus.org/articles/15/3161/2022/}, + doi = {10.5194/gmd-15-3161-2022} +} + +@book{lord2014, + title = {An introduction to computational stochastic PDEs}, + author = {Lord, Gabriel J and Powell, Catherine E and Shardlow, Tony}, + volume = {50}, + year = {2014}, + publisher = {Cambridge University Press} +} + +@book{montero2015, + title = {Spatial and spatio-temporal geostatistical modeling and kriging}, + author = {Montero, Jos{\'e}-Mar{\'\i}a and Fern{\'a}ndez-Avil{\'e}s, Gema and Mateu, Jorge}, + year = {2015}, + publisher = {John Wiley \& Sons} +} + +@article{pirot2015, + title = {Influence of conceptual model uncertainty on contaminant transport forecasting in braided river aquifers}, + author = {Pirot, Guillaume and Renard, Philippe and Huber, Emanuel and Straubhaar, Julien and Huggenberger, Peter}, + journal = {Journal of Hydrology}, + volume = {531}, + pages = {124--141}, + year = {2015} +} + +@article{PlotsJL, + doi = {https://doi.org/10.5334/jors.431}, + url = {https://openresearchsoftware.metajnl.com/articles/10.5334/jors.431/}, + author = {Christ, Simon and Schwabeneder, Daniel and Rackauckas, Christopher and Borregaard, Michael Krabbe and Breloff, Thomas}, + keywords = {Graphics (cs.GR), FOS: Computer and information sciences, FOS: Computer and information sciences, I.3.3}, + title = {Plots.jl -- a user extendable plotting API for the julia programming language}, + publisher = {Journal of Open Research Software}, + year = {2023}, + copyright = {Creative Commons Attribution 4.0 International} +} + +@misc{randomfields, + author = {Schlather, M}, + title = {RandomFields}, + year = {2022}, + publisher = {CRAN}, + journal = {CRAN repository}, + url = {https://cran.r-project.org/web/packages/RandomFields/index.html} +} + +@article{robbe2021, + title = {Enhanced multi-index Monte Carlo by means of multiple semicoarsened multigrid for anisotropic diffusion problems}, + author = {Robbe, Pieterjan and Nuyens, Dirk and Vandewalle, Stefan}, + journal = {Numerical Linear Algebra with Applications}, + volume = {28}, + number = {3}, + pages = {e2281}, + year = {2021} +} + +@article{stephenson2006, + title = {Adaptive Markov random fields for example-based super-resolution of faces}, + author = {Stephenson, Todd A and Chen, Tsuhan}, + journal = {EURASIP Journal on Advances in Signal Processing}, + volume = {2006}, + pages = {1--11}, + year = {2006} +} + +@article{wu2023, + title={Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface}, + author={Wu, Hao and Greer, Sarah Y and O’Malley, Daniel}, + journal={Scientific Reports}, + volume={13}, + number={1}, + pages={718}, + year={2023} +} \ No newline at end of file diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 0000000..2450a81 --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,48 @@ +--- +title: 'GaussianRandomFields.jl: A Julia package to generate and sample from Gaussian random fields' +tags: + - Julia + - random fields + - uncertainty quantification + - statistics +authors: + - name: Pieterjan Robbe + orcid: 0000-0002-6254-8245 + affiliation: "1" +affiliations: + - name: KU Leuven, Belgium + index: 1 +date: 27 May 2023 +bibliography: paper.bib +--- + +# Summary + +Random fields are used to represent spatially-varying uncertainty, and are commonly used as training data in uncertainty quantification and machine learning applications. GaussianRandomFields.jl is a Julia [@bezanson2017] software package to generate and sample from Gaussian random fields. It offers support for well-known covariance functions, such as Gaussian, exponential and Matérn covariances [@bishop2006; @chiles2012; @montero2015], as well as user-defined covariance structures defined on arbitrary domains. The package implements most common methods to generate samples from these random fields, including the Cholesky factorization, the Karhunen-Loève expansion, and the circulant embedding method [@lord2014]. GaussianRandomFields.jl makes use of [Plots.jl](https://github.com/JuliaPlots/Plots.jl) [@PlotsJL] to quickly visualize samples of the random fields. + +# Statement of need + +Gaussian random fields are used by scientists to describe complex patterns and structures emerging in nature. They provide a statistical tool for describing a vast amount of different structures found in various applications such as electronics [@cui2018], geostatistics [@pirot2015], machine learning [@stephenson2006] and cosmology [@chiang2000]. Random fields can be viewed as the extension from random variables to random functions, taking random values at different points Gaussian random fields are particularly attractive, because they only require two parameters to be fully defined: a mean value and a covariance function. GaussianRandomFields.jl provides Julia implementations of stationary separable and non-separable isotropic and anisotropic Gaussian random fields. It has been used in a number of recent works, including [@blondeel2020], [@robbe2021] and [@wu2023]. + +There are already some packages for Gaussian random field generation available in R [@randomfields] and Python [@GSTools]. GaussianRandomFields.jl offers a native Julia implementation. As such, it benefits from the performance advantage of Julia, and provides a convenient unified API for different covariance functions by leveraging multiple dispatch. A particular example are the covariance functions from [KernelFunctions.jl](https://github.com/JuliaGaussianProcesses/KernelFunctions.jl), which can easily be linked to the Gaussian random field generators implemented in this package. + +# Usage + +The following example is an excerpt from the [tutorial](https://pieterjanrobbe.github.io/GaussianRandomFields.jl/stable/) (see \autoref{fig:samples}). + +```julia +using GaussianRandomFields, Plots + +cov = CovarianceFunction(2, Exponential(.5)) +pts = range(0, stop=1, length=1001) +grf = GaussianRandomField(cov, CirculantEmbedding(), pts, pts, minpadding=2001) +heatmap(grf) +``` + +![Three realizations of a two-dimensional Gaussian random field with exponential covariance function.\label{fig:samples}](exponential_grf.jpg) + +# Acknowledgements + +The author acknowledges the help of David Widmann implementing a dimension-independent variant of the circulant embedding method using `@generated` functions. + +# References \ No newline at end of file From fecf77b976bfa5ee35949bb55bfb14ca6dfc3d1b Mon Sep 17 00:00:00 2001 From: PieterjanR Date: Sat, 27 May 2023 18:04:56 +0200 Subject: [PATCH 2/9] Added pic and github action for paper --- .github/workflows/draft-pdf.yml | 23 +++++++++++++++++++++++ paper/exponential_grf.jpg | Bin 0 -> 403018 bytes 2 files changed, 23 insertions(+) create mode 100644 .github/workflows/draft-pdf.yml create mode 100644 paper/exponential_grf.jpg diff --git a/.github/workflows/draft-pdf.yml b/.github/workflows/draft-pdf.yml new file mode 100644 index 0000000..6563593 --- /dev/null +++ b/.github/workflows/draft-pdf.yml @@ -0,0 +1,23 @@ +on: [push] + +jobs: + paper: + runs-on: ubuntu-latest + name: Paper Draft + steps: + - name: Checkout + uses: actions/checkout@v3 + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + # This should be the path to the paper within your repo. + paper-path: paper/paper.md + - name: Upload + uses: actions/upload-artifact@v1 + with: + name: paper + # This is the output path where Pandoc will write the compiled + # PDF. 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ze+F=aFDeH!*$G}TM31BVGz{oXs0!_sx#9p8IoL6P70uUh1=O#%X~dDDTM!AZbf6vp z>Tl#=%y33nw4<0fF~`-Jglk`Nw* zxJzqW8{nZByfv$)%@=)O!y4+M48%#hWk>vI&!piJ;ZmS8f)6l82->F9l#xL^$+s}Z z2fW@q?hAv)3*g}-Ih$*fT>52IXmz?m44G}($j>KhY^gMAvFk$fXiT^h!!&}5t?;#} zxDc}CBUSD&JS)99fGuNckIOX=|A-EQ*F3Voj=)=`;go+QjJr!DaMl#-B@Y zGAX|6uofChQCkyn0wShvSy42HML0>0x|#gruI#4iNQ!~WIoEhnG%7T5sE}4=>c9P` zR6&1ZAekioxG``KAm^gIlOXY5X-`hF{C_74>q)g#FdG|*>1$oyrOm6&Uz5O6F|2ZK iy>6`97h}_{3hIme$sbr#o4uZziXZ<^O~du~yZ-_>FiDUA literal 0 HcmV?d00001 From 2d2b738b125336374ea6d9720c3cde1f7f3b2962 Mon Sep 17 00:00:00 2001 From: PieterjanR Date: Sat, 27 May 2023 18:27:30 +0200 Subject: [PATCH 3/9] Small updates to paper --- paper/paper.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 2450a81..d70380e 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -22,13 +22,13 @@ Random fields are used to represent spatially-varying uncertainty, and are commo # Statement of need -Gaussian random fields are used by scientists to describe complex patterns and structures emerging in nature. They provide a statistical tool for describing a vast amount of different structures found in various applications such as electronics [@cui2018], geostatistics [@pirot2015], machine learning [@stephenson2006] and cosmology [@chiang2000]. Random fields can be viewed as the extension from random variables to random functions, taking random values at different points Gaussian random fields are particularly attractive, because they only require two parameters to be fully defined: a mean value and a covariance function. GaussianRandomFields.jl provides Julia implementations of stationary separable and non-separable isotropic and anisotropic Gaussian random fields. It has been used in a number of recent works, including [@blondeel2020], [@robbe2021] and [@wu2023]. +Random fields are used by scientists to describe complex patterns and structures emerging in nature. They provide a statistical tool for describing a vast amount of different structures found in various applications such as electronics [@cui2018], geostatistics [@pirot2015], machine learning [@stephenson2006] and cosmology [@chiang2000]. Random fields can be viewed as an extension from random variables to random functions, in the sense that the random field takes random values at each point in the domain where it is defined. Gaussian random fields are particularly attractive, because they only require two parameters to be fully specified: a mean value and a covariance function. GaussianRandomFields.jl provides Julia implementations of Gaussian random fields with stationary separable and non-separable isotropic and anisotropic covariance functions. It has been used in a number of recent works, including [@blondeel2020], [@robbe2021] and [@wu2023]. -There are already some packages for Gaussian random field generation available in R [@randomfields] and Python [@GSTools]. GaussianRandomFields.jl offers a native Julia implementation. As such, it benefits from the performance advantage of Julia, and provides a convenient unified API for different covariance functions by leveraging multiple dispatch. A particular example are the covariance functions from [KernelFunctions.jl](https://github.com/JuliaGaussianProcesses/KernelFunctions.jl), which can easily be linked to the Gaussian random field generators implemented in this package. +Other packages for Gaussian random field generation are available in R [@randomfields] and Python [@GSTools]. GaussianRandomFields.jl offers a native Julia implementation. As such, it benefits from the performance advantage of Julia, and provides a convenient unified API for different covariance functions by leveraging multiple dispatch. A particular example are the covariance functions from [KernelFunctions.jl](https://github.com/JuliaGaussianProcesses/KernelFunctions.jl), which can easily be linked to the Gaussian random field generators implemented in this package. # Usage -The following example is an excerpt from the [tutorial](https://pieterjanrobbe.github.io/GaussianRandomFields.jl/stable/) (see \autoref{fig:samples}). +The full API of GaussianRandomFields.jl is described in detail in the [documentation](https://pieterjanrobbe.github.io/GaussianRandomFields.jl/stable/). We also provide a tutorial [tutorial](https://pieterjanrobbe.github.io/GaussianRandomFields.jl/stable/tutorial) with various examples detailing how to define, sample from, and visualze Gaussian random fields. The following example is an excerpt from the tutorial. We refer to \autoref{fig:samples} for an illustration. ```julia using GaussianRandomFields, Plots From 14ce789fd1e85d012151e3473a72c7675ed7cce1 Mon Sep 17 00:00:00 2001 From: PieterjanR Date: Sat, 27 May 2023 18:30:27 +0200 Subject: [PATCH 4/9] Fixed typo in paper --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index d70380e..2350519 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -28,7 +28,7 @@ Other packages for Gaussian random field generation are available in R [@randomf # Usage -The full API of GaussianRandomFields.jl is described in detail in the [documentation](https://pieterjanrobbe.github.io/GaussianRandomFields.jl/stable/). We also provide a tutorial [tutorial](https://pieterjanrobbe.github.io/GaussianRandomFields.jl/stable/tutorial) with various examples detailing how to define, sample from, and visualze Gaussian random fields. The following example is an excerpt from the tutorial. We refer to \autoref{fig:samples} for an illustration. +The full API of GaussianRandomFields.jl is described in detail in the [documentation](https://pieterjanrobbe.github.io/GaussianRandomFields.jl/stable/). We also provide a tutorial [tutorial](https://pieterjanrobbe.github.io/GaussianRandomFields.jl/stable/tutorial) with various examples detailing how to define, sample from, and visualize Gaussian random fields. The following example is an excerpt from the tutorial. We refer to \autoref{fig:samples} for an illustration. ```julia using GaussianRandomFields, Plots From 8955eb87f6cc019968490009ae83968fd7a904e5 Mon Sep 17 00:00:00 2001 From: PieterjanR Date: Sat, 27 May 2023 19:02:29 +0200 Subject: [PATCH 5/9] Fixed typo in paper --- paper/paper.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/paper.md b/paper/paper.md index 2350519..569b408 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -28,7 +28,7 @@ Other packages for Gaussian random field generation are available in R [@randomf # Usage -The full API of GaussianRandomFields.jl is described in detail in the [documentation](https://pieterjanrobbe.github.io/GaussianRandomFields.jl/stable/). We also provide a tutorial [tutorial](https://pieterjanrobbe.github.io/GaussianRandomFields.jl/stable/tutorial) with various examples detailing how to define, sample from, and visualize Gaussian random fields. The following example is an excerpt from the tutorial. We refer to \autoref{fig:samples} for an illustration. +The full API of GaussianRandomFields.jl is described in detail in the [documentation](https://pieterjanrobbe.github.io/GaussianRandomFields.jl/stable/). We also provide a [tutorial](https://pieterjanrobbe.github.io/GaussianRandomFields.jl/stable/tutorial) with various examples detailing how to define, sample from, and visualize Gaussian random fields. The following example is an excerpt from the tutorial. We refer to \autoref{fig:samples} for an illustration. ```julia using GaussianRandomFields, Plots From 25ed8beddf20acfe37f78424add7cb770bf70cfb Mon Sep 17 00:00:00 2001 From: PieterjanR Date: Mon, 12 Jun 2023 06:53:02 +0300 Subject: [PATCH 6/9] Updated references --- paper/paper.bib | 23 +++++++++++++++-------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index 84a05cd..fc2cfab 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -19,6 +19,7 @@ @book{bishop2006 @article{blondeel2020, title = {p-refined multilevel quasi-Monte Carlo for Galerkin finite element methods with applications in civil engineering}, + doi = {10.3390/a13050110}, author = {Blondeel, Philippe and Robbe, Pieterjan and Van hoorickx, C{\'e}dric and Fran{\c{c}}ois, Stijn and Lombaert, Geert and Vandewalle, Stefan}, journal = {Algorithms}, volume = {13}, @@ -29,6 +30,7 @@ @article{blondeel2020 @article{chiang2000, title = {Phase information and the evolution of cosmological density perturbations}, + doi = {10.1046/j.1365-8711.2000.03086.x}, author = {Chiang, Lung-Yih and Coles, Peter}, journal = {Monthly Notices of the Royal Astronomical Society}, volume = {311}, @@ -47,6 +49,7 @@ @book{chiles2012 @inproceedings{cui2018, title = {Uncertainty quantification of electronic and photonic ICs with non-Gaussian correlated process variations}, + doi = {10.1145/3240765.3240860}, author = {Cui, Chunfeng and Zhang, Zheng}, booktitle = {Proceedings of the International Conference on Computer-Aided Design}, pages = {1--8}, @@ -82,6 +85,7 @@ @book{montero2015 @article{pirot2015, title = {Influence of conceptual model uncertainty on contaminant transport forecasting in braided river aquifers}, + doi = {10.1016/j.jhydrol.2015.07.036}, author = {Pirot, Guillaume and Renard, Philippe and Huber, Emanuel and Straubhaar, Julien and Huggenberger, Peter}, journal = {Journal of Hydrology}, volume = {531}, @@ -90,8 +94,8 @@ @article{pirot2015 } @article{PlotsJL, - doi = {https://doi.org/10.5334/jors.431}, url = {https://openresearchsoftware.metajnl.com/articles/10.5334/jors.431/}, + doi = {10.5334/jors.431}, author = {Christ, Simon and Schwabeneder, Daniel and Rackauckas, Christopher and Borregaard, Michael Krabbe and Breloff, Thomas}, keywords = {Graphics (cs.GR), FOS: Computer and information sciences, FOS: Computer and information sciences, I.3.3}, title = {Plots.jl -- a user extendable plotting API for the julia programming language}, @@ -111,6 +115,7 @@ @misc{randomfields @article{robbe2021, title = {Enhanced multi-index Monte Carlo by means of multiple semicoarsened multigrid for anisotropic diffusion problems}, + doi = {10.1002/nla.2281}, author = {Robbe, Pieterjan and Nuyens, Dirk and Vandewalle, Stefan}, journal = {Numerical Linear Algebra with Applications}, volume = {28}, @@ -121,6 +126,7 @@ @article{robbe2021 @article{stephenson2006, title = {Adaptive Markov random fields for example-based super-resolution of faces}, + doi = {10.1155/asp/2006/31062}, author = {Stephenson, Todd A and Chen, Tsuhan}, journal = {EURASIP Journal on Advances in Signal Processing}, volume = {2006}, @@ -129,11 +135,12 @@ @article{stephenson2006 } @article{wu2023, - title={Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface}, - author={Wu, Hao and Greer, Sarah Y and O’Malley, Daniel}, - journal={Scientific Reports}, - volume={13}, - number={1}, - pages={718}, - year={2023} + title = {Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface}, + doi = {10.1038/s41598-022-26898-1}, + author = {Wu, Hao and Greer, Sarah Y and O’Malley, Daniel}, + journal = {Scientific Reports}, + volume = {13}, + number = {1}, + pages = {718}, + year = {2023} } \ No newline at end of file From 8bd9cec743bcf45c1daf059d58af364ed85dea42 Mon Sep 17 00:00:00 2001 From: PieterjanR Date: Tue, 8 Aug 2023 10:04:41 -0700 Subject: [PATCH 7/9] Added reference for performance claim --- paper/paper.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paper/paper.md b/paper/paper.md index 569b408..7d6a93e 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -24,7 +24,7 @@ Random fields are used to represent spatially-varying uncertainty, and are commo Random fields are used by scientists to describe complex patterns and structures emerging in nature. They provide a statistical tool for describing a vast amount of different structures found in various applications such as electronics [@cui2018], geostatistics [@pirot2015], machine learning [@stephenson2006] and cosmology [@chiang2000]. Random fields can be viewed as an extension from random variables to random functions, in the sense that the random field takes random values at each point in the domain where it is defined. Gaussian random fields are particularly attractive, because they only require two parameters to be fully specified: a mean value and a covariance function. GaussianRandomFields.jl provides Julia implementations of Gaussian random fields with stationary separable and non-separable isotropic and anisotropic covariance functions. It has been used in a number of recent works, including [@blondeel2020], [@robbe2021] and [@wu2023]. -Other packages for Gaussian random field generation are available in R [@randomfields] and Python [@GSTools]. GaussianRandomFields.jl offers a native Julia implementation. As such, it benefits from the performance advantage of Julia, and provides a convenient unified API for different covariance functions by leveraging multiple dispatch. A particular example are the covariance functions from [KernelFunctions.jl](https://github.com/JuliaGaussianProcesses/KernelFunctions.jl), which can easily be linked to the Gaussian random field generators implemented in this package. +Other packages for Gaussian random field generation are available in R [@randomfields] and Python [@GSTools]. GaussianRandomFields.jl offers a native Julia implementation. As such, it benefits from the performance advantage of Julia, see [@bezanson2017], and provides a convenient unified API for different covariance functions by leveraging multiple dispatch. A particular example are the covariance functions from [KernelFunctions.jl](https://github.com/JuliaGaussianProcesses/KernelFunctions.jl), which can easily be linked to the Gaussian random field generators implemented in this package. # Usage @@ -45,4 +45,4 @@ heatmap(grf) The author acknowledges the help of David Widmann implementing a dimension-independent variant of the circulant embedding method using `@generated` functions. -# References \ No newline at end of file +# References From cfc21ae98fc3bbe87c5b14311fc2d90d1079b045 Mon Sep 17 00:00:00 2001 From: Mehmet Hakan Satman Date: Thu, 31 Aug 2023 20:06:29 +0300 Subject: [PATCH 8/9] update bibliography --- paper/paper.bib | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/paper/paper.bib b/paper/paper.bib index fc2cfab..2306ecb 100644 --- a/paper/paper.bib +++ b/paper/paper.bib @@ -18,7 +18,7 @@ @book{bishop2006 } @article{blondeel2020, - title = {p-refined multilevel quasi-Monte Carlo for Galerkin finite element methods with applications in civil engineering}, + title = {p-refined multilevel quasi-{M}onte {C}arlo for {G}alerkin finite element methods with applications in civil engineering}, doi = {10.3390/a13050110}, author = {Blondeel, Philippe and Robbe, Pieterjan and Van hoorickx, C{\'e}dric and Fran{\c{c}}ois, Stijn and Lombaert, Geert and Vandewalle, Stefan}, journal = {Algorithms}, @@ -40,15 +40,16 @@ @article{chiang2000 } @book{chiles2012, - title = {Geostatistics: modeling spatial uncertainty}, + title = {Geostatistics: {M}odeling spatial uncertainty}, author = {Chiles, Jean-Paul and Delfiner, Pierre}, volume = {713}, year = {2012}, + doi = {10.1016/s0098-3004(00)00063-7}, publisher = {John Wiley \& Sons} } @inproceedings{cui2018, - title = {Uncertainty quantification of electronic and photonic ICs with non-Gaussian correlated process variations}, + title = {Uncertainty quantification of electronic and photonic ICs with non-{G}aussian correlated process variations}, doi = {10.1145/3240765.3240860}, author = {Cui, Chunfeng and Zhang, Zheng}, booktitle = {Proceedings of the International Conference on Computer-Aided Design}, @@ -58,7 +59,7 @@ @inproceedings{cui2018 @article{GSTools, author = {M\"uller, S. and Sch\"uler, L. and Zech, A. and He{\ss}e, F.}, - title = {\texttt{GSTools} v1.3: a toolbox for geostatistical modelling in Python}, + title = {\texttt{GSTools} v1.3: a toolbox for geostatistical modelling in {P}ython}, journal = {Geoscientific Model Development}, volume = {15}, year = {2022}, @@ -98,7 +99,7 @@ @article{PlotsJL doi = {10.5334/jors.431}, author = {Christ, Simon and Schwabeneder, Daniel and Rackauckas, Christopher and Borregaard, Michael Krabbe and Breloff, Thomas}, keywords = {Graphics (cs.GR), FOS: Computer and information sciences, FOS: Computer and information sciences, I.3.3}, - title = {Plots.jl -- a user extendable plotting API for the julia programming language}, + title = {Plots.jl -- {A} user extendable plotting API for the {J}ulia programming language}, publisher = {Journal of Open Research Software}, year = {2023}, copyright = {Creative Commons Attribution 4.0 International} @@ -114,7 +115,7 @@ @misc{randomfields } @article{robbe2021, - title = {Enhanced multi-index Monte Carlo by means of multiple semicoarsened multigrid for anisotropic diffusion problems}, + title = {Enhanced multi-index {M}onte {C}arlo by means of multiple semicoarsened multigrid for anisotropic diffusion problems}, doi = {10.1002/nla.2281}, author = {Robbe, Pieterjan and Nuyens, Dirk and Vandewalle, Stefan}, journal = {Numerical Linear Algebra with Applications}, From 36774d11e314f06c3386e388543f8cd955185624 Mon Sep 17 00:00:00 2001 From: PieterjanR Date: Thu, 31 Aug 2023 10:25:51 -0700 Subject: [PATCH 9/9] Bump version number --- Project.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Project.toml b/Project.toml index c8572d6..6c6f1b7 100644 --- a/Project.toml +++ b/Project.toml @@ -1,6 +1,6 @@ name = "GaussianRandomFields" uuid = "e4b2fa32-6e09-5554-b718-106ed5adafe9" -version = "2.2.2" +version = "2.2.3" [deps] Arpack = "7d9fca2a-8960-54d3-9f78-7d1dccf2cb97"

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