3 papers
cs.LG2026
Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Models
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…
stat.ML2025
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…
cs.LG2024
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…