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cs.LG2026
Dissipative Lagrangian Neural Networks for the Interpretable Modelling of Passive Mechanical Systems
Rob Seabourne, Alice Cicirello
In this paper, we propose the Dissipative Lagrangian Neural Network (dLNN), a continuous-time system identification technique that separately learns the conservative and dissipativ…
cs.LG2026
Fast-varying Natural Frequencies and Damping Ratio Identification for Linear Time-Varying System
Melisa Bozaci, Alice Cicirello
This work proposes a physics-enhanced machine learning approach for the system identification of Linear Time-Varying (LTV) systems under time-varying operating conditions in terms…
cs.LG2026
In-Context Residual Calibration for Uncertainty Quantification of Energy Time Series over Graphs
Keivan Faghih Niresi, Alice Cicirello, Olga Fink
Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems. Spatial-temporal graph neural networks (STGNNs) have r…