21 citations · 21 across the 3 of their papers we have counts for
5 papers
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…
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…
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…
Adversarial Disentanglement by Backpropagation with Physics-Informed Variational Autoencoder
Ioannis Christoforos Koune, Alice Cicirello
Inference and prediction under partial knowledge of a physical system is challenging, particularly when multiple confounding sources influence the measured response. Explicitly acc…
Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations
Alice Cicirello
This position paper takes a broad look at Physics-Enhanced Machine Learning (PEML) -- also known as Scientific Machine Learning -- with particular focus to those PEML strategies de…