17 citations · 32 across the 20 of their papers we have counts for
21 papers
Joint State-Parameter Inference Enhances Estimation Performance in Model-Based Digital Therapeutics for Type 1 Diabetes
Milad Banitalebi Dehkordi, Vihangkumar V. Naik, Manas Mejari +2
Blood glucose estimation is the cornerstone of model-based decision support (DS) and Automated Insulin Delivery (AID) systems. Control systems that rely on physiologic/compartmenta…
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…
Learning reduced-order latent linear models for Kalman filtering of nonlinear systems
Manas Mejari, Milad Banitalebi Dehkordi, Dario Piga
We propose a filtering-oriented end-to-end learning framework to identify reduced-order models explicitly tailored for state estimation in high-dimensional nonlinear systems. An au…
ASIA: an Autonomous System Identification Agent
Dario Piga, Marco Forgione
Over the years, research in system identification has provided a rich set of methods for learning dynamical models, together with well-established theoretical guarantees. In practi…
Rao-Blackwellized Stein Gradient Descent for Joint State-Parameter Estimation
Milad Banitalebi Dehkordi, Manas Mejari, Dario Piga
We present a filtering framework for online joint state estimation and parameter identification in nonlinear, time-varying systems. The algorithm uses a Rao-Blackwellization techni…
Nonlinear System Identification Nano-drone Benchmark
Riccardo Busetto, Elia Cereda, Marco Forgione +3
We introduce a benchmark for system identification based on 75k real-world samples from the Crazyflie 2.1 Brushless nano-quadrotor, a sub-50g aerial vehicle widely adopted in robot…
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…
Learning Low-Dimensional Embeddings for Black-Box Optimization
Riccardo Busetto, Manas Mejari, Marco Forgione +2
When gradient-based methods are impractical, black-box optimization (BBO) provides a valuable alternative. However, BBO often struggles with high-dimensional problems and limited t…
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…
In-Context Learning for Zero-Shot Speed Estimation of BLDC motors
Alessandro Colombo, Riccardo Busetto, Valentina Breschi +3
Accurate speed estimation in sensorless brushless DC motors is essential for high-performance control and monitoring, yet conventional model-based approaches struggle with system n…
dynoGP: Deep Gaussian Processes for dynamic system identification
Alessio Benavoli, Dario Piga, Marco Forgione +1
In this work, we present a novel approach to system identification for dynamical systems, based on a specific class of Deep Gaussian Processes (Deep GPs). These models are construc…
One controller to rule them all
Riccardo Busetto, Valentina Breschi, Marco Forgione +2
Imagine having a system to control and only know that it belongs to a certain class of dynamical systems. Would it not be amazing to simply plug in a controller and have it work as…
Bias correction and instrumental variables for direct data-driven model-reference control
Manas Mejari, Valentina Breschi, Simone Formentin +1
Managing noisy data is a central challenge in direct data-driven control design. We propose an approach for synthesizing model-reference controllers for linear time-invariant (LTI)…
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…
In-context learning of state estimators
Riccardo Busetto, Valentina Breschi, Marco Forgione +2
State estimation has a pivotal role in several applications, including but not limited to advanced control design. Especially when dealing with nonlinear systems state estimation i…
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…
Shedding Light on the Ageing of Extra Virgin Olive Oil: Probing the Impact of Temperature with Fluorescence Spectroscopy and Machine Learning Techniques
Francesca Venturini, Silvan Fluri, Manas Mejari +3
This work systematically investigates the oxidation of extra virgin olive oil (EVOO) under accelerated storage conditions with UV absorption and total fluorescence spectroscopy. Wi…
Data-Driven Computation of Robust Invariant Sets and Gain-Scheduled Controllers for Linear Parameter-Varying Systems
Manas Mejari, Ankit Gupta, Dario Piga
We present a direct data-driven approach to synthesize robust control invariant (RCI) sets and their associated gain-scheduled feedback control laws for linear parameter-varying (L…
From system models to class models: An in-context learning paradigm
Marco Forgione, Filippo Pura, Dario Piga
Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavior of other systems within the same cla…