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Simone Formentin

Polytechnic University of Milan

Piazza Leonardo da Vinci

Milan, 20100

Italy

SCHOLARLY PAPERS

4

DOWNLOADS

127

TOTAL CITATIONS

0

Scholarly Papers (4)

1.

Twin-in-the-Loop Observers Tuning via Gradient-Information Bayesian Optimization with Line Search

Number of pages: 11 Posted: 13 Dec 2025
affiliation not provided to SSRN, RWTH Aachen University, RWTH Aachen University, affiliation not provided to SSRN, RWTH Aachen University and Polytechnic University of Milan
Downloads 45 (1,121,190)

Abstract:

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Digital Twin, Twin-in-the-Loop, observer design, Bayesian optimization, Vehicle State Estimation, Gradient Descent

2.

Bayesian Optimization with executableDigital Twins: Fast controller tuning with multi-source information

Number of pages: 14 Posted: 15 Jan 2026
Giacomo Delcaro, Simone Gabrielli and Simone Formentin
affiliation not provided to SSRN, affiliation not provided to SSRN and Polytechnic University of Milan
Downloads 32 (1,359,097)

Abstract:

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Bayesian Optimization, gaussian process, Multi-Output Gaussian Process, Acquisition Function, Digital twin, Twin-in-the-Loop, Controller Tuning, sim-to-real

3.

Regularized GLISp for sensor-guided human-in-the-loop optimization1

Number of pages: 6 Posted: 20 Nov 2025
MATTEO CERCOLA, Michele Lomuscio, Dario Piga and Simone Formentin
Polytechnic University of Milan, Polytechnic University of Milan, affiliation not provided to SSRN and Polytechnic University of Milan
Downloads 29 (1,332,057)

Abstract:

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Human-in-the-Loop optimization, active preference learning, preferential Bayesian, optimization, grey-box optimization

4.

Efficient Reinforcement Learning from Human Feedback via Bayesian Preference Inference

Number of pages: 7 Posted: 25 Nov 2025
MATTEO CERCOLA, Valeria Capretti and Simone Formentin
Polytechnic University of Milan, Polytechnic University of Milan and Polytechnic University of Milan
Downloads 21 (1,448,301)

Abstract:

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Human-in-the-Loop optimization, Reinforcement Learning from Human Feedback (RLHF), Preferential Bayesian Optimization (PBO), Active learning, Preference-based optimization, Large Language Models (LLMs), High-dimensional optimization.