Francesco Cadini is assistant professor at the Department of Mechanical Engineering of the Politecnico di Milano. His research interests include the quantitative treatment of uncertainties for risk assessment and mitigation in mechanical, aerospace and structural systems. His activities are mainly focused on i) diagnostic and prognostic (structural health monitoring - SHM - and prognostics and health management - PHM) and ii) rare-event structural reliability analysis, robust (RDO) and reliability-based design optimization (RBDO). The main algorithmic tools employed and/or developed are: i) stochastic filtering (sequential Monte Carlo algorithms - particle filters, etc.) for state estimation, ii) efficient Monte Carlo sampling of rare events (variance reduction methods), iii) machine learning (neural networks, deep learning, kriging, support vector machines, etc.) for classification, regression, model identification and time series prediction and iv) advanced optimization tools (e.g., evolutionary algorithms).
Metrics overview (02/2021)
- Approximately 100 publications in International journals (63) and proceedings of international conferences (Scopus: 72).
- H-index: 19, H-index (10y): 12, H-index (5y): 7 (Scopus).
- Citations: 1047, cit. (10y): 653, cit. (5y): 278 (Scopus).
- January 2018 – present. Politecnico di Milano, Milano, Italy. Assistant professor (scientific area ING-IND/14) at the Department of Mechanics of the Politecnico di Milano.
- July 2005 – December 2017. Politecnico di Milano, Milano, Italy. Assistant professor (scientific area ING-IND/18) at the Department of Energy of the Politecnico di Milano.
- May 2006. Politecnico di Milano - Department of Nuclear Engineering, Milano, Italy. Ph.D. in “Radiation science and engineering”.
- September 2003. University of California, Los Angeles (UCLA), USA. Master’s in science in Aerospace Engineering at the Henri Samueli School of Engineering and Applied Science.
- February 2000. Politecnico di Milano - Department of Nuclear Engineering, Milano, Italy. Degree in Nuclear Engineering.
Thesis CMV 009/2023 - Prognostics and Health Management of lithium-ion batteries exploiting multi-physics digital twin (DT) and machine learning (ML) - Prof. Francesco Cadini, Yiqi Jia, Lorenzo Brancato
Thesis CMV 029/2023 - Numerical analysis of composite representative volume elements and development of machine learning-based surrogate models - Prof. Marco Giglio, Prof. Andrea Manes, Prof. Francesco Cadini