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pro vyhledávání: '"A Forgione"'
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 of a whole class of systems. Once trained, this meta-model is fed wit
Externí odkaz:
http://arxiv.org/abs/2410.03291
Autor:
Bazzi, Manuel Bianchi, Shahid, Asad Ali, Agia, Christopher, Alora, John, Forgione, Marco, Piga, Dario, Braghin, Francesco, Pavone, Marco, Roveda, Loris
The landscape of Deep Learning has experienced a major shift with the pervasive adoption of Transformer-based architectures, particularly in Natural Language Processing (NLP). Novel avenues for physical applications, such as solving Partial Different
Externí odkaz:
http://arxiv.org/abs/2409.11815
With a specific emphasis on control design objectives, achieving accurate system modeling with limited complexity is crucial in parametric system identification. The recently introduced deep structured state-space models (SSM), which feature linear d
Externí odkaz:
http://arxiv.org/abs/2403.14833
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 model generalization and robustness in scenarios characterized by data sca
Externí odkaz:
http://arxiv.org/abs/2403.05164
In-context system identification aims at constructing meta-models to describe classes of systems, differently from traditional approaches that model single systems. This paradigm facilitates the leveraging of knowledge acquired from observing the beh
Externí odkaz:
http://arxiv.org/abs/2312.04083
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 is a nontrivial task, often entailing approximations and challenging fi
Externí odkaz:
http://arxiv.org/abs/2312.04509
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 class? This central question drives the study presented in this paper. In
Externí odkaz:
http://arxiv.org/abs/2308.13380
Autor:
Forgione, Marco, Piga, Dario
Effective quantification of uncertainty is an essential and still missing step towards a greater adoption of deep-learning approaches in different applications, including mission-critical ones. In particular, investigations on the predictive uncertai
Externí odkaz:
http://arxiv.org/abs/2304.06349
Autor:
Lucia Tudini, Andrea Forgione
Publikováno v:
Aquaculture Journal, Vol 4, Iss 2, Pp 55-75 (2024)
The Italian shellfish industry mainly comprises clams, mussels, and oysters. While clam production thrives and Italy leads Europe, mussel farming faces economic challenges. Oyster production is relatively new and holds potential. Sustainable developm
Externí odkaz:
https://doaj.org/article/7bc466e615344b69b932564f9ac01520
Autor:
Matteo Pavone, Laure Waeldin, Barbara Seeliger, Nicolò Bizzarri, Didier Mutter, Delphine Jarnet, Antonello Forgione, Noel Georges, Cherif Akladios, Giovanni Scambia, Jacques Marescaux, Lise Lecointre, Denis Querleu
Publikováno v:
World Journal of Surgical Oncology, Vol 22, Iss 1, Pp 1-8 (2024)
Abstract Background Radio(chemo)therapy is often required in pelvic malignancies (cancer of the anus, rectum, cervix). Direct irradiation adversely affects ovarian and endometrial function, compromising the fertility of women. While ovarian transposi
Externí odkaz:
https://doaj.org/article/d1982be040934cdba62014fcc025a007