Ph.D. in Ingegneria Aerospaziale , 36^th cycle (2020-2023)

Ph.D. obtained in 2024

Dissertation:

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

Tutors:

Paolo Maggiore Matteo Davide Lorenzo Dalla Vedova Laura Mainini

Profile

Research topic

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

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.

Publications

Works published during the Ph.D. View all publications in Porto@Iris

Di Fiore, F.; Nardelli, M.; Mainini, L. (2024)
Active Learning and Bayesian Optimization: A Unified Perspective to Learn with a Goal. In: ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING. ISSN 1134-3060
Contributo su Rivista
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 (2024)
FREEDOM: Validated Method for the Rapid Assessment of Incipient Multimodal Faults of Complex Aerospace Systems. In: AIAA JOURNAL, vol. 62. ISSN 1533-385X
Contributo su Rivista
DI FIORE, Francesco (2024)
Advanced Computational Methods for Design, Diagnostics and Prognostics of Aerospace Systems. relatore: MAGGIORE, Paolo; DALLA VEDOVA, MATTEO DAVIDE LORENZO; , 36. XXXVI Ciclo, P.: 231
Doctoral Thesis
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; Mainini, Laura (2023)
NM-MF: Non-Myopic Multifidelity Framework for Constrained Multi-Regime Aerodynamic Optimization. In: AIAA JOURNAL, vol. 61, pp. 1270-1280. ISSN 0001-1452
Contributo su Rivista
Di Fiore, Francesco; Mainini, Laura (2022)
DOMAIN-AWARE MULTIFIDELITY LEARNING FOR DESIGN OPTIMIZATION. In: 8th European Congress on Computational Methods in Applied Sciences and Engineering, Oslo, Norvegia, 5-9 Giugno 2022
Contributo in Atti di Convegno (Proceeding)
Di Fiore, Francesco; Mainini, Laura (2022)
Domain-Aware Active Learning for Multifidelity Optimization. In: 15th World Congress on Computational Mechanics, Virtuale, 31 Luglio - 5 Agosto 2022
Contributo in Atti di Convegno (Proceeding)
Di Fiore, Francesco; Maggiore, Paolo; Mainini, Laura (2021)
Multifidelity domain-aware learning for the design of re-entry vehicles. In: STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, vol. Special Issue dedicated to Dr. Raphael T. Haftka.. ISSN 1615-147X
Contributo su Rivista
DI FIORE, Francesco; Maggiore, Paolo; Mainini, Laura (2021)
Multifidelity modeling for the design of re-entry capsules. In: AIAA Scitech 2021, Virtuale, 11-15 Gennaio 2021
Contributo in Atti di Convegno (Proceeding)
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)
Dalla Vedova, M. D. L.; Di Fiore, F. (2020)
Proposal of a new simplified coulomb friction model applied to electrohydraulic servomechanisms. In: INTERNATIONAL JOURNAL OF MECHANICS AND CONTROL, vol. 21, pp. 179-188. ISSN 1590-8844
Contributo su Rivista

More publicationsLess publications

Francesco Di Fiore

Ph.D. in Ingegneria Aerospaziale , 36th cycle (2020-2023)