Publications by Federica Bragone
Peer reviewed
Articles
[1]
F. Bragone et al., "Automatic learning analysis of flow-induced birefringence in cellulose nanofibrils," Journal of Computational Science, vol. 85, 2025.
[2]
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.
[3]
F. Bragone et al., "Physics-informed neural networks for modelling power transformer’s dynamic thermal behaviour," Electric power systems research, vol. 211, pp. 108447-108447, 2022.
Conference papers
[4]
F. Bragone et al., "Time Series Predictions Based on PCA and LSTM Networks : A Framework for Predicting Brownian Rotary Diffusion of Cellulose Nanofibrils," in Computational Science – ICCS 2024 - 24th International Conference, 2024, Proceedings, 2024, pp. 209-223.
[5]
T. Laneryd et al., "Physics Informed Neural Networks for Power Transformer Dynamic Thermal Modelling," in IFAC Papersonline, 2022, pp. 49-54.
[6]
F. Bragone et al., "Physics-Informed Neural Networks for Modeling Cellulose Degradation in Power Transformers," in 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022.
[7]
K. Oueslati et al., "Physics-Informed Neural Networks for modelling insulation paper degradation in Power Transformers," in 2022 IEEE International Conference on Electrical Sciences and Technologies in Maghreb (CISTEM), 2022.
[8]
O. Welin Odeback et al., "Physics-Informed Neural Networks for prediction of transformer's temperature distribution," in 2022 21ST IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS, ICMLA, 2022, pp. 1579-1586.
[9]
O. Welin Odeback et al., "Physics-Informed Neural Networks for prediction of transformer’s temperature distribution," in 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022.
[10]
D. Bogatov Wilkman et al., "Self-Supervised Transformer Networks for Error Classification of Tightening Traces," in 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 2022.
Non-peer reviewed
Theses
[11]
F. Bragone, "Scientific Machine Learning for Forward and Inverse Problems : Physics-Informed Neural Networks and Machine Learning Algorithms with Applications to Dynamical Systems," Doctoral thesis Stockholm : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2025:44, 2025.
[12]
F. Bragone, "Physics-Informed Neural Networks and Machine Learning Algorithms for Sustainability Advancements in Power Systems Components," Licentiate thesis Stockholm, Sweden : KTH Royal Institute of Technology, TRITA-EECS-AVL, 2023:69, 2023.
Other
[13]
J. Vicens Figueres et al., "$PINN - a Domain Decomposition Method for Bayesian Physics-Informed Neural Networks," (Manuscript).
[14]
F. Tembo et al., "Data-Driven vs Traditional Approaches to Power Transformer’s Top-Oil Temperature Estimation," (Manuscript).
[15]
F. Bragone et al., "Discovering Partially Known Ordinary Differential Equations : a Case Study on the Chemical Kinetics of Cellulose Degradation," (Manuscript).
[16]
[17]
S. Li et al., "Optimal Sensor Placement in Power Transformers Using Physics-Informed Neural Networks," (Manuscript).
[18]
F. Bragone et al., "Unsupervised Learning Analysis of Flow-Induced Birefringence in Nanocellulose: Differentiating Materials and Concentrations," (Manuscript).
Latest sync with DiVA:
2025-09-13 22:59:40 UTC