Publikationer av Federica Bragone
Refereegranskade
Artiklar
[1]
K. Morozovska et al., "Trade-offs of wind power production: A study on the environmental implications of raw materials mining in the life cycle of wind turbines," Journal of Cleaner Production, vol. 460, 2024.
[2]
F. Bragone et al., "Physics-informed neural networks for modelling power transformer’s dynamic thermal behaviour," Electric power systems research, vol. 211, s. 108447-108447, 2022.
Konferensbidrag
[3]
F. Bragone et al., "Time Series Predictions Based on PCA and LSTM Networks: A Framework for Predicting Brownian Rotary Diffusion of Cellulose Nanofibrils," i Computational Science – ICCS 2024 - 24th International Conference, 2024, Proceedings, 2024, s. 209-223.
[4]
T. Laneryd et al., "Physics Informed Neural Networks for Power Transformer Dynamic Thermal Modelling," i IFAC Papersonline, 2022, s. 49-54.
[5]
F. Bragone et al., "Physics-Informed Neural Networks for Modeling Cellulose Degradation in Power Transformers," i 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022.
[6]
K. Oueslati et al., "Physics-Informed Neural Networks for modelling insulation paper degradation in Power Transformers," i 2022 IEEE International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), 2022.
[7]
O. Welin Odeback et al., "Physics-Informed Neural Networks for prediction of transformer's temperature distribution," i 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA, 2022, s. 1579-1586.
[8]
O. Welin Odeback et al., "Physics-Informed Neural Networks for prediction of transformer’s temperature distribution," i 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022.
[9]
D. Bogatov Wilkman et al., "Self-Supervised Transformer Networks for Error Classification of Tightening Traces," i 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022.
Icke refereegranskade
Avhandlingar
[10]
F. Bragone, "Physics-Informed Neural Networks and Machine Learning Algorithms for Sustainability Advancements in Power Systems Components," Licentiatavhandling Stockholm, Sweden : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2023:69, 2023.
Övriga
[11]
F. Bragone et al., "Unsupervised Learning Analysis of Flow-Induced Birefringence in Nanocellulose: Differentiating Materials and Concentrations," (Manuskript).
Senaste synkning med DiVA:
2024-08-15 01:02:46