Predicting wind-driven spatial deposition through simulated color images using deep autoencoders.

Fernández-Godino MG, Lucas DD, Kong Q

Open source

DOI: 10.1038/s41598-023-28590-4
Published: 2023 Jan 25
Container: Scientific reports
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1038/s41598-023-28590-4,
  title = {Predicting wind-driven spatial deposition through simulated color images using deep autoencoders.},
  author = {Fernández-Godino MG and Lucas DD and Kong Q},
  year = {2023},
  journal = {Scientific reports},
  doi = {10.1038/s41598-023-28590-4},
  url = {https://doi.org/10.1038/s41598-023-28590-4}
}

RIS

TY  - JOUR
TI  - Predicting wind-driven spatial deposition through simulated color images using deep autoencoders.
AU  - Fernández-Godino MG
AU  - Lucas DD
AU  - Kong Q
PY  - 2023
JO  - Scientific reports
DO  - 10.1038/s41598-023-28590-4
UR  - https://doi.org/10.1038/s41598-023-28590-4
ER  -

APA

MG, F., DD, L., & Q, K. (2023). Predicting wind-driven spatial deposition through simulated color images using deep autoencoders.. Scientific reports. https://doi.org/10.1038/s41598-023-28590-4

Source records

pubmed · retrieved 2026-11-10T18:53:07.611Z
europe-pmc · retrieved 2026-11-10T18:53:07.667Z
doaj · retrieved 2026-11-10T18:53:07.611Z