Artificial Intelligence
Data Science
Algorithms
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Algorithms
Data Science
Artificial Intelligence
[Archimedes Talks Series]Machine-learning for structural dynamics and structural health monitoring
Dates
2024-07-23 11:00 - 13:00
Venue
Artemidos 1 - Amphitheater
Title: Machine-learning for structural dynamics and structural health monitoring
Presenter:
Dr. George Tsialiamanis (Dynamics Research Group, University of Sheffield, UK)
Abstract: In recent years, the field of structural dynamics has transitioned from physics-based approaches to the inclusion
of data in the process of modelling systems. Data-driven techniques, including machine learning, have offered solutions to structural-dynamics problems which would be extremely challenging or even impossible to solve with traditional approaches, e.g. non-linear
modal analysis. A major subfield of structural dynamics which is inherently connected to the acquisition of data, that of structural health monitoring (SHM), is founded on the use of machine-learning techniques. The field of SHM aims at maintaining the operational
condition of structures to ensure safety and minimise potential economic and societal costs. Data acquired from sensors placed on structures and a wide range of algorithms are exploited to gain insights in the condition of the structure, to identify and localise
potential damage, and to perform prognosis regarding the remaining useful life of the structure.
Bio: Dr. Tsialiamanis is a lecturer (Assistant professor) in the Dynamics Research Group (DRG) of the Mechanical Engineering Department of the University of Sheffield.
Bio: Dr. Tsialiamanis is a lecturer (Assistant professor) in the Dynamics Research Group (DRG) of the Mechanical Engineering Department of the University of Sheffield.
Dr. Tsialiamanis completed his MEng at the Civil Engineering School of the National Technical University of Athens. He then joined the DRG of the University of Sheffield as a PhD student and a Marie Skłodowska-Curie early stage researcher of the Dynamic Virtualisation (DyVirt) project. He has been a visiting researcher at ETH Zurich and has acquired funding from the non-destructive evaluation division of the Engineering Institute of the Los Alamos National Laboratory and for an extended research visit. Dr. Tsialiamanis is also a member of the steering board committee of the Welsh Digital Twin Network, which aims at developing a functional digital twin of Wales to improve the everyday life of people in the region.
The research of the DRG is focused on modelling structural dynamics. A great part of the research is conducted on the structural health monitoring of structures which is inseparably connected to the acquisition and exploitation of data. The group employs machine-learning algorithms to deal with situations where physical knowledge is absent and the formalisation of the physics is challenging because of the presence of uncertainties. A great deal of research is also conducted on the Verification and Validation of models, which is based on performing experiments to confirm the accuracy and robustness of models. For this purpose, the Laboratory of Verification and Validation (LVV) is used, where equipment is available to test structures under varying environmental conditions, such as varying temperatures, wind loadings, wave loading, etc.
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Vision
To position Greece as a leading player in AI and Data Science
Mission
To build an AI Excellence Hub in Greece where the international research community can connect, groundbreaking ideas can thrive, and the next generation of scientists emerges, shaping a brighter future for Greece and the world
Welcome to ARCHIMEDES, a vibrant research hub connecting the global AI and Data Science research community fostering groundbreaking research in Greece and beyond. Its dedicated core team, comprising lead researchers, affiliated researchers, Post-Docs, PhDs and interns, is committed to advancing basic and applied research in Artificial Intelligence and its supporting disciplines, including Algorithms, Statistics, Learning Theory, and Game Theory organized around 8 core research areas. By collaborating with Greek and Foreign Universities and Research Institutes, ARCHIMEDES disseminates its research findings fostering knowledge exchange and providing enriching opportunities for students. Leveraging AI to address real-world challenges, ARCHIMEDES promotes innovation within the Greek ecosystem and extends its societal impact. Established in January 2022, as a research unit of the Athena Research Center with support from the Committee Greece 2021, ARCHIMEDES is funded for its first four years by the EU Recovery and Resilience Facility (RRF).
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Archimedes Academic Fellow and a third-year PhD student at the National and Kapodistrian University of Athens in Greece, Andreas Lolos recently travelled to Tucson in Arizona, USA, and presented the paper "SGPMIL: Sparse Gaussian Process Multiple Instance Learning" at the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2026).
6 Accepted Papers at ICLR 2026
11 March 2026
The Archimedes research unit of the Athena Research Center in Greece has six papers accepted at this year's International Conference on Learning Representations (ICLR) gathering, including one oral presentation. The acceptance rate for posters was about 25,8% and about 1,1% for oral presentations. Please bear in mind that ICLR, along with NeurIPS and ICML, is considered one of the three top-tier artificial intelligence and machine learning conferences in the world.
The project “ARCHIMEDES Unit: Research in Artificial Intelligence, Data Science and Algorithms” with code OPS 5154714 is implemented by the National Recovery and Resilience Plan “Greece 2.0” and is funded by the European Union – NextGenerationEU.
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