[Dottorcomp] Seminari di Matematica Applicata, 14/04/2026, Paolo Manfredi

[Lorenzo Tamellini]

Seminari di Matematica Applicata - Dipartimento di Matematica "F. Casorati"
e Istituto CNR-IMATI "E. Magenes" di Pavia.

Martedì 14 Aprile 2026, alle ore 15 precise, presso la sala conferenze IMATI

*Paolo Manfredi (Politecnico di Torino)*

terrà il seguente seminario

*Titolo: *Data-efficient uncertainty quantification and optimization of
electronic systems using machine learning

*Abstract: *This talk provides an overview over the utilization of Gaussian
process regression (GPR) for the data-efficient uncertainty quantification,
yield analysis, and optimization of electronic systems. GPR, also known as
Kriging, is a nonparametric method belonging to the class of kernel machine
learning regressions. As such, it does not assume a predetermined
functional form, but it rather constructs the model from data. This makes
it more flexible and suitable for building surrogate models of
computationally expensive systems characterized by a large number of
independent input design parameters. In particular, we will discuss the
calculation of statistical moments and the prediction confidence, explore
active learning strategies for the iterative acquisition of optimal
training datasets, and introduce a hybrid technique that combines GPR and
polynomial chaos expansion (PCE).