Francesco Di Fiore

Ph.D. candidate in Ingegneria Aerospaziale , 36^th cycle (2020-2023)
Department of Mechanical and Aerospace Engineering (DIMEAS)

francesco.difiore@polito.it

Profile

PhD

Thesis title

Advanced Computational Methods for Design, Diagnostics and Prognostics of Aerospace Systems

Research topic

Multidisciplinary design optimization, multifidelity methods, physics-based machine learning.

Tutors

Research interests

Concurrent Engineering / Aerospace Systems Engineering

Design and Analysis of Aircraft and RPAS

Hypersonic Vehicles / Reusable Access to Space and Re-Entry Systems

Biography

Francesco Di Fiore is a PhD candidate in aerospace engineering at Politecnico di Torino. His research ranges from developing advanced multidisciplinary and multifidelity methods for accelerated high-fidelity design optimization and diagnostics to applying those techniques to the design of aircraft, re-entry vehicles, and other engineering systems. His research on those themes has appeared or is forthcoming in leading journals of aerospace engineering and computational science including Nature Scientific Reports, AIAA Journal, Structural and Multidisciplinary Optimization, and more. Francesco has presented his academic works in numerous international conferences such as the AIAA SciTech 2021 and 2023, AIAA Aviation 2022 and 2023, WCSMO 2021, ECCOMAS 2022, WCCM 2022, ECT 2022, GIMC SIMAI 2022, IACM CFC 2023, and MARINE 2023. Francesco has undertaken research activities of relevance and leveraged by NATO STO AVT-331 RTG on goal-driven, multi-fidelity approaches for military vehicle system-level design and AVT-354 RTG on multi-fidelity methods for military vehicle design, and collaborated with international communities and investigators from United States, Turkey, United Kingdom, Switzerland, and Italy.

Skills

ERC sectors

PE8_1 - Aerospace engineering

Publications

Latest publications View all publications in Porto@Iris

Di Fiore, F.; Mainini, L. (2024)
Physics-aware multifidelity Bayesian optimization: A generalized formulation. In: COMPUTERS & STRUCTURES, vol. 296. ISSN 0045-7949
Contributo su Rivista
Di Fiore, Francesco; Berri, Pier Carlo; Mainini, Laura (2023)
FREEDOM: Validated Method for the Rapid Assessment of Incipient Multimodal Faults of Complex Aerospace Systems. In: AIAA JOURNAL. ISSN 1533-385X
Contributo su Rivista
Di Fiore, Francesco; Berri, Pier Carlo; Mainini, Laura (2023)
Multifidelity Framework for the Efficient Identification of Damages in Complex Aerospace Systems. In: AIAA AVIATION 2023 Forum, San Diego, CA, 12-16 Giugno 2023, pp. 4449-4464
Contributo in Atti di Convegno (Proceeding)
DI FIORE, Francesco; Mainini, Laura (2023)
Non-myopic multipoint multifidelity Bayesian framework for multidisciplinary design. In: SCIENTIFIC REPORTS, vol. 13. ISSN 2045-2322
Contributo su Rivista
DI FIORE, Francesco; Maggiore, Paolo; Mainini, Laura (2021)
Multifidelity Learning for the Design of Re-Entry Capsules. In: 14th World Congress of Structural and Multidisciplinary Optimization, Virtuale, 13-18 Giugno 2021, pp. 191-191
Contributo in Atti di Convegno (Proceeding)