Author

D. Piga

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#artificial intelligence Preprint Oct 2026

In-Context Learning for System Identification: Probabilistic Formulation, Long-Sequence Handling, and Benchmark Validation

Traditional system identification typically involves two steps: initially, training data is processed to estimate a model of the system of interest; then, the model is used to predict outputs for new input queries. In contrast, recent in-context learning approaches learn a meta model that maps directly from training da...

Matteo Rufolo, D. Piga, Filippo Pura et al. · 0 citations

Jul 2026

Learning reduced-order latent linear models for Kalman filtering of nonlinear systems

A filtering-oriented end-to-end learning framework to identify reduced-order models explicitly tailored for state estimation in high-dimensional nonlinear systems and quantifies probabilistic bounds on the performance gap between full-order and reduced-order filters using conformal predictions, which do not require ass...

Manas Mejari, M. B. Dehkordi, D. Piga · arXiv.org · 0 citations

Jul 2026

Variational meta-learning inference for low dimensional neural system identification

This work proposes a fully probabilistic extension of the manifold meta-learning framework, based on amortized Variational Inference, where a generative prior over the low-dimensional parameter manifold is learned.

Matteo Rufolo, D. Piga, M. Forgione · arXiv.org · 0 citations