A high-dimensional steady-state structural framework for regional transmission interface capacity planning using physics-embedded graph representation learning.
- DOI
- 10.1038/s41598-026-50578-z
- Published
- 2026 May 27
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-50578-z,
title = {A high-dimensional steady-state structural framework for regional transmission interface capacity planning using physics-embedded graph representation learning.},
author = {Zhang D and Mu Y and Guan D and Xue W},
year = {2026},
journal = {Scientific reports},
doi = {10.1038/s41598-026-50578-z},
url = {https://doi.org/10.1038/s41598-026-50578-z}
}RIS
TY - JOUR TI - A high-dimensional steady-state structural framework for regional transmission interface capacity planning using physics-embedded graph representation learning. AU - Zhang D AU - Mu Y AU - Guan D AU - Xue W PY - 2026 JO - Scientific reports DO - 10.1038/s41598-026-50578-z UR - https://doi.org/10.1038/s41598-026-50578-z ER -
APA
D, Z., Y, M., D, G., & W, X. (2026). A high-dimensional steady-state structural framework for regional transmission interface capacity planning using physics-embedded graph representation learning.. Scientific reports. https://doi.org/10.1038/s41598-026-50578-z
Source records
- pubmed · retrieved 2026-09-27T06:22:25.058Z