Conveners
Keynote: Prof. Stefano Sfarra, University of L’Aquila, Italy
- Gennaro Cardone (Università di Napoli Federico II)
Description
Professor Stefano Sfarra attained a Ph.D. title in mechanical, management and energy engineering at the University of L’Aquila (UNIVAQ), Italy, in 2011. Following the achievement of the Ph.D., he was a research fellow at UNIVAQ until 2017, before becoming a researcher in October of the same year.
He carried out research and teaching periods abroad at prestigious institutions all over the world. He was also an invited-scientific researcher at Tomsk Polytechnic University (Tomsk, Russia), as well as a member of several scientific committees at international conferences.
He is also an editor of Infrastructures (MDPI), and Sensors (MDPI). Since December 2020, he is the editor-in-chief (EiC) of the Quantitative InfraRed Thermography (QIRT) Journal (Taylor & Francis). He is deeply involved in the non-destructive evaluation and characterization of materials, especially using optical and infrared vision non-destructive testing techniques, numerical simulations centered on heat transfer phenomena (by Comsol© Multiphysics), development of ad hoc scripts in Matlab©environment, and inverse thermal modelling.
In these research areas, Prof. Sfarra authored or co-authored more than 300 papers in Journals and International Conferences. He also have written seven chapters in Books. He is currently acting as a reviewer of around 50 scientific journals; he is principal investigator, collaborator and local contact person in international research projects.
He is also a member of Associazione MASTER, Associazione Italiana della Fisica Tecnica and Associazione Italiana Proprietà Termofisiche.
He received many awards, mainly focused on scientific recognition.
In October 2020, he became associate professor at UNIVAQ. He has been an adjunct professor at Laval University (Canada).
Infrared thermography captures surface temperature fields that can visualize subsurface defects, yet conventional analyses are hampered by sensor noise and imperfect heat-conduction models. The physics-guided (informed) neural reconstruction framework can be developed embedding heat-transfer priors within a deep network to recover a denoised, physically consistent background from raw thermal...
