Skip to content

Commit

Permalink
Merge pull request #5558 from openjournals/joss.06915
Browse files Browse the repository at this point in the history
Merging automatically
  • Loading branch information
editorialbot authored Jun 30, 2024
2 parents 103bebc + 848a031 commit 83a1d26
Show file tree
Hide file tree
Showing 6 changed files with 922 additions and 0 deletions.
279 changes: 279 additions & 0 deletions joss.06915/10.21105.joss.06915.crossref.xml
Original file line number Diff line number Diff line change
@@ -0,0 +1,279 @@
<?xml version="1.0" encoding="UTF-8"?>
<doi_batch xmlns="http://www.crossref.org/schema/5.3.1"
xmlns:ai="http://www.crossref.org/AccessIndicators.xsd"
xmlns:rel="http://www.crossref.org/relations.xsd"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
version="5.3.1"
xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schemas/crossref5.3.1.xsd">
<head>
<doi_batch_id>20240630190818-874b95e974a92d53e70a540e92ede78c070bf868</doi_batch_id>
<timestamp>20240630190818</timestamp>
<depositor>
<depositor_name>JOSS Admin</depositor_name>
<email_address>[email protected]</email_address>
</depositor>
<registrant>The Open Journal</registrant>
</head>
<body>
<journal>
<journal_metadata>
<full_title>Journal of Open Source Software</full_title>
<abbrev_title>JOSS</abbrev_title>
<issn media_type="electronic">2475-9066</issn>
<doi_data>
<doi>10.21105/joss</doi>
<resource>https://joss.theoj.org</resource>
</doi_data>
</journal_metadata>
<journal_issue>
<publication_date media_type="online">
<month>06</month>
<year>2024</year>
</publication_date>
<journal_volume>
<volume>9</volume>
</journal_volume>
<issue>98</issue>
</journal_issue>
<journal_article publication_type="full_text">
<titles>
<title>DiffeRT2d: A Differentiable Ray Tracing Python
Framework for Radio Propagation</title>
</titles>
<contributors>
<person_name sequence="first" contributor_role="author">
<given_name>Jérome</given_name>
<surname>Eertmans</surname>
<ORCID>https://orcid.org/0000-0002-5579-5360</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Claude</given_name>
<surname>Oestges</surname>
<ORCID>https://orcid.org/0000-0002-0902-4565</ORCID>
</person_name>
<person_name sequence="additional"
contributor_role="author">
<given_name>Laurent</given_name>
<surname>Jacques</surname>
<ORCID>https://orcid.org/0000-0002-6261-0328</ORCID>
</person_name>
</contributors>
<publication_date>
<month>06</month>
<day>30</day>
<year>2024</year>
</publication_date>
<pages>
<first_page>6915</first_page>
</pages>
<publisher_item>
<identifier id_type="doi">10.21105/joss.06915</identifier>
</publisher_item>
<ai:program name="AccessIndicators">
<ai:license_ref applies_to="vor">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="am">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
<ai:license_ref applies_to="tdm">http://creativecommons.org/licenses/by/4.0/</ai:license_ref>
</ai:program>
<rel:program>
<rel:related_item>
<rel:description>Software archive</rel:description>
<rel:inter_work_relation relationship-type="references" identifier-type="doi">10.5281/zenodo.12600658</rel:inter_work_relation>
</rel:related_item>
<rel:related_item>
<rel:description>GitHub review issue</rel:description>
<rel:inter_work_relation relationship-type="hasReview" identifier-type="uri">https://github.com/openjournals/joss-reviews/issues/6915</rel:inter_work_relation>
</rel:related_item>
</rel:program>
<doi_data>
<doi>10.21105/joss.06915</doi>
<resource>https://joss.theoj.org/papers/10.21105/joss.06915</resource>
<collection property="text-mining">
<item>
<resource mime_type="application/pdf">https://joss.theoj.org/papers/10.21105/joss.06915.pdf</resource>
</item>
</collection>
</doi_data>
<citation_list>
<citation key="adam">
<article_title>Adam: A method for stochastic
optimization</article_title>
<author>Kingma</author>
<cYear>2017</cYear>
<unstructured_citation>Kingma, D. P., &amp; Ba, J. (2017).
Adam: A method for stochastic optimization.
https://arxiv.org/abs/1412.6980</unstructured_citation>
</citation>
<citation key="deepmind2020jax">
<article_title>The DeepMind JAX Ecosystem</article_title>
<author>DeepMind</author>
<cYear>2020</cYear>
<unstructured_citation>DeepMind, Babuschkin, I., Baumli, K.,
Bell, A., Bhupatiraju, S., Bruce, J., Buchlovsky, P., Budden, D., Cai,
T., Clark, A., Danihelka, I., Dedieu, A., Fantacci, C., Godwin, J.,
Jones, C., Hemsley, R., Hennigan, T., Hessel, M., Hou, S., … Viola, F.
(2020). The DeepMind JAX Ecosystem.
http://github.com/google-deepmind</unstructured_citation>
</citation>
<citation key="eertmans2024eucap">
<article_title>Fully differentiable ray tracing via
discontinuity smoothing for radio network optimization</article_title>
<author>Eertmans</author>
<journal_title>2024 18th european conference on antennas and
propagation (EuCAP)</journal_title>
<doi>10.23919/EuCAP60739.2024.10501570</doi>
<cYear>2024</cYear>
<unstructured_citation>Eertmans, J., Jacques, L., &amp;
Oestges, C. (2024). Fully differentiable ray tracing via discontinuity
smoothing for radio network optimization. 2024 18th European Conference
on Antennas and Propagation (EuCAP), 1–5.
https://doi.org/10.23919/EuCAP60739.2024.10501570</unstructured_citation>
</citation>
<citation key="fpt">
<article_title>A novel ray tracing algorithm for scenarios
comprising pre-ordered multiple planar reflectors, straight wedges, and
vertexes</article_title>
<author>Puggelli</author>
<journal_title>IEEE Transactions on Antennas and
Propagation</journal_title>
<issue>8</issue>
<volume>62</volume>
<doi>10.1109/TAP.2014.2323961</doi>
<cYear>2014</cYear>
<unstructured_citation>Puggelli, F., Carluccio, G., &amp;
Albani, M. (2014). A novel ray tracing algorithm for scenarios
comprising pre-ordered multiple planar reflectors, straight wedges, and
vertexes. IEEE Transactions on Antennas and Propagation, 62(8),
4336–4341.
https://doi.org/10.1109/TAP.2014.2323961</unstructured_citation>
</citation>
<citation key="imagemethod">
<article_title>Ray tracing for radio propagation modeling:
Principles and applications</article_title>
<author>Yun</author>
<journal_title>IEEE Access</journal_title>
<volume>3</volume>
<doi>10.1109/ACCESS.2015.2453991</doi>
<cYear>2015</cYear>
<unstructured_citation>Yun, Z., &amp; Iskander, M. F.
(2015). Ray tracing for radio propagation modeling: Principles and
applications. IEEE Access, 3, 1089–1100.
https://doi.org/10.1109/ACCESS.2015.2453991</unstructured_citation>
</citation>
<citation key="jax2018github">
<article_title>JAX: Composable transformations of
Python+NumPy programs</article_title>
<author>Bradbury</author>
<cYear>2024</cYear>
<unstructured_citation>Bradbury, J., Frostig, R., Hawkins,
P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A.,
VanderPlas, J., Wanderman-Milne, S., &amp; Zhang, Q. (2024). JAX:
Composable transformations of Python+NumPy programs (Version 0.4.28).
http://github.com/google/jax</unstructured_citation>
</citation>
<citation key="jaxtyping2024github">
<article_title>jaxtyping: Type annotations and runtime
checking for shape and dtype of JAX arrays, and PyTrees</article_title>
<author>Kidger</author>
<cYear>2024</cYear>
<unstructured_citation>Kidger, P. (2024). jaxtyping: Type
annotations and runtime checking for shape and dtype of JAX arrays, and
PyTrees (Version 0.2.29).
http://github.com/patrick-kidger/jaxtyping</unstructured_citation>
</citation>
<citation key="kidger2021equinox">
<article_title>Equinox: Neural networks in JAX via callable
PyTrees and filtered transformations</article_title>
<author>Kidger</author>
<journal_title>Differentiable Programming workshop at Neural
Information Processing Systems 2021</journal_title>
<cYear>2021</cYear>
<unstructured_citation>Kidger, P., &amp; Garcia, C. (2021).
Equinox: Neural networks in JAX via callable PyTrees and filtered
transformations. Differentiable Programming Workshop at Neural
Information Processing Systems 2021.</unstructured_citation>
</citation>
<citation key="mlhelsinki">
<article_title>Learning to sample ray paths for faster
point-to-point ray tracing</article_title>
<author>Eertmans</author>
<journal_title>COST INTERACT 8th Meeting (Helsinki, from
2024/06/17 to 2024/06/20)</journal_title>
<cYear>2024</cYear>
<unstructured_citation>Eertmans, J., Oestges, C., Jacques,
L., &amp; others. (2024). Learning to sample ray paths for faster
point-to-point ray tracing. In COST INTERACT 8th Meeting (Helsinki, from
2024/06/17 to 2024/06/20). http://
hdl.handle.net/2078/288635</unstructured_citation>
</citation>
<citation key="mpt-eucap2023">
<article_title>Min-Path-Tracing: A diffraction aware
alternative to image method in ray tracing</article_title>
<author>Eertmans</author>
<journal_title>2023 17th european conference on antennas and
propagation (EuCAP)</journal_title>
<doi>10.23919/EuCAP57121.2023.10132934</doi>
<cYear>2023</cYear>
<unstructured_citation>Eertmans, J., Oestges, C., &amp;
Jacques, L. (2023). Min-Path-Tracing: A diffraction aware alternative to
image method in ray tracing. 2023 17th European Conference on Antennas
and Propagation (EuCAP), 1–5.
https://doi.org/10.23919/EuCAP57121.2023.10132934</unstructured_citation>
</citation>
<citation key="opal">
<article_title>Opal: An open source ray-tracing propagation
simulator for electromagnetic characterization</article_title>
<author>Egea-Lopez</author>
<journal_title>PLOS ONE</journal_title>
<issue>11</issue>
<volume>16</volume>
<doi>10.1371/journal.pone.0260060</doi>
<cYear>2021</cYear>
<unstructured_citation>Egea-Lopez, E., Molina-Garcia-Pardo,
J. M., Lienard, M., &amp; Degauque, P. (2021). Opal: An open source
ray-tracing propagation simulator for electromagnetic characterization.
PLOS ONE, 16(11), 1–19.
https://doi.org/10.1371/journal.pone.0260060</unstructured_citation>
</citation>
<citation key="pylayers">
<article_title>Advanced radio channel
simulator</article_title>
<author>Uguen</author>
<cYear>2014</cYear>
<unstructured_citation>Uguen, B., Amiot, N., Laaraiedh, M.,
Mhedhbi, M., Avrillon, S., Burghelea, R., Plouhinec, E., Talom, F. T.,
Chaluyman, T., &amp; Lei, Y. (2014). Advanced radio channel simulator
(Version 0.5).
http://github.com/pylayers/pylayers</unstructured_citation>
</citation>
<citation key="sionnart">
<article_title>Sionna RT: Differentiable ray tracing for
radio propagation modeling</article_title>
<author>Hoydis</author>
<journal_title>2023 IEEE globecom workshops (GC
wkshps)</journal_title>
<doi>10.1109/GCWkshps58843.2023.10465179</doi>
<cYear>2023</cYear>
<unstructured_citation>Hoydis, J., Aoudia, F. A., Cammerer,
S., Nimier-David, M., Binder, N., Marcus, G., &amp; Keller, A. (2023).
Sionna RT: Differentiable ray tracing for radio propagation modeling.
2023 IEEE Globecom Workshops (GC Wkshps), 317–321.
https://doi.org/10.1109/GCWkshps58843.2023.10465179</unstructured_citation>
</citation>
<citation key="tensorflow">
<article_title>TensorFlow: Large-scale machine learning on
heterogeneous systems</article_title>
<author>Abadi</author>
<cYear>2015</cYear>
<unstructured_citation>Abadi, M., Agarwal, A., Barham, P.,
Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J.,
Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard,
M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., … Zheng, X. (2015).
TensorFlow: Large-scale machine learning on heterogeneous systems.
https://www.tensorflow.org/</unstructured_citation>
</citation>
</citation_list>
</journal_article>
</journal>
</body>
</doi_batch>
Binary file added joss.06915/10.21105.joss.06915.pdf
Binary file not shown.
Loading

0 comments on commit 83a1d26

Please sign in to comment.