17 citations · 21 across the 16 of their papers we have counts for
7 papers · 1 filter
Variational meta-learning inference for low dimensional neural system identification
Matteo Rufolo, Dario Piga, Marco Forgione
Deep learning has proven highly effective for nonlinear system identification, but heavily parameterized neural networks are prone to overfitting in low-data regimes and lack relia…
Distributionally robust minimization in meta-learning for system identification
Matteo Rufolo, Dario Piga, Marco Forgione
Meta learning aims at learning how to solve tasks, and thus it allows to estimate models that can be quickly adapted to new scenarios. This work explores distributionally robust mi…
Manifold meta-learning for reduced-complexity neural system identification
Marco Forgione, Ankush Chakrabarty, Dario Piga +2
System identification has greatly benefited from deep learning techniques, particularly for modeling complex, nonlinear dynamical systems with partially unknown physics where tradi…
Enhanced Transformer architecture for in-context learning of dynamical systems
Matteo Rufolo, Dario Piga, Gabriele Maroni +1
Recently introduced by some of the authors, the in-context identification paradigm aims at estimating, offline and based on synthetic data, a meta-model that describes the behavior…
Model order reduction of deep structured state-space models: A system-theoretic approach
Marco Forgione, Manas Mejari, Dario Piga
With a specific emphasis on control design objectives, achieving accurate system modeling with limited complexity is crucial in parametric system identification. The recently intro…
Synthetic data generation for system identification: leveraging knowledge transfer from similar systems
Dario Piga, Matteo Rufolo, Gabriele Maroni +2
This paper addresses the challenge of overfitting in the learning of dynamical systems by introducing a novel approach for the generation of synthetic data, aimed at enhancing mode…
On the adaptation of in-context learners for system identification
Dario Piga, Filippo Pura, Marco Forgione
In-context system identification aims at constructing meta-models to describe classes of systems, differently from traditional approaches that model single systems. This paradigm f…