Matzner, R;
Luo, R;
Zervas, G;
Bayvel, P;
(2022)
Expanding Graph Neural Networks for Ultra-Fast Optical Core Network Throughput Prediction to Large Node Scales.
In:
2022 European Conference on Optical Communication, ECOC 2022.
IEEE: Basel, Switzerland.
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Abstract
Using maximum achievable throughput as an objective, message passing neural networks (MPNN) are applied to larger optical networks (25-100 nodes), enabling physical properties-aware large-scale topology optimisation in record time, reducing computation time by 5 orders of magnitude, with close to perfect throughput correlation (ρ = 0.986).
| Type: | Proceedings paper |
|---|---|
| Title: | Expanding Graph Neural Networks for Ultra-Fast Optical Core Network Throughput Prediction to Large Node Scales |
| Event: | European Conference and Exhibition on Optical Communication 2022 |
| ISBN-13: | 9781957171159 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://ieeexplore.ieee.org/document/9979635 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Correlation, Computational modeling, Optical recording, Training data, Optical computing, Optical fiber networks, Predictive models |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10164289 |
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