Andrea Leone

ESR 4:
Andrea Leone

joined the THREAD project in August 2020 (read his welcome message)

My name is Andrea Leone and I come from Italy. I have a master’s degree in Mathematical Engineering and a bachelor’s degree in Physics, both from University of L’Aquila (Italy). During my master’s studies, I attended courses in a wide range of topics, from mathematical modelling in engineering to optimisation theory. Meanwhile, I developed an interest in data science and machine learning, so I decided to write my master’s thesis on a subject related to deep learning. I also participated in the Erasmus+ Programme and I spent 4 months at NTNU (Trondheim, Norway), where I studied the interpretation of deep learning neural networks as discretizations of an optimal control problem.

I have a keen interest in mathematical modelling and numerical analysis as well as machine learning and I believe that the THREAD project on data driven modelling of beams will highly improve my expertise in these fields. In particular, I am motivated to work on the applications of structure preserving numerical methods and geometric numerical integration to slender, flexible structures, since they have a key role in the performance of many engineering systems. I think that this is an exciting and challenging research topic, with significant industrial applicability.

Furthermore, I truly appreciate the interdisciplinary research environment of the THREAD network, based on the collaboration of mathematicians and engineers, and this is reflected in my academic background. Therefore, I believe that this is an invaluable opportunity for me to investigate fundamental modelling problems in a strong international academic environment.

Andrea Leone, August 2020

Host Institution
Norwegian University of Science and Technology (Norway)


Incorporate available data information in models for slender structures (e.g. oil reisers). Usage: 1) validation of existing physical models; 2) new, fully data driven models; 3) models being partly data driven, partly obtained by Cosserat theory; 4) studies of structural fatigue, validation by measurements, analysis of material properties and physical characteristics.

Expected Results

Improved models for cable simulation (discretised and implemented in a code). The designed models will be analysed using techniques based on shape analysis on Lie groups where the motion of the cables is considered as a space-time dependent curve on the Lie group SE(3) (in collaboration with ESR5). The deformation and motion of the cable can be seen as the geodesic curve in an infinite dimensional manifold. The features of this deformation can be determined by optimal control problems or alternatively, using machine learning and deep neural networks. ESR4 benefits from data generated in virtual experiments (ESR3, ESR6) and real experiments (ESR11).


Articles in peer-reviewed scientific journals

  • Ergys Çokaj, Halvor Gustad, Andrea Leone, Per Thomas Moe, Lasse Moldestad (2024): Supervised time series classification for anomaly detection in subsea engineering; Journal of Computational Dynamics, published online; DOI: 10.3934/jcd.2024019, see also link
  • Elena Celledoni, Andrea Leone, Davide Murari, Brynjulf Owren (2023): Learning Hamiltonians of constrained mechanical systems; Journal of Computational and Applied Mathematics 417, 114608; DOI: 10.1016/, see also link
  • Elena Celledoni, Ergys Çokaj, Andrea Leone, Davide Murari, Brynjulf Owren (2022): Lie group integrators for mechanical systems; International Journal of Computer Mathematics 99(1), 58–88; DOI: 10.1080/00207160.2021.1966772, see also link

Publications in conference proceedings

  • Elena Celledoni, Ergys Çokaj, Andrea Leone, Davide Murari, Brynjulf Owren (2022): Dynamics of the N-fold Pendulum in the Framework of Lie Group Integrators. In: Matthias Ehrhardt, Michael Günther (ed.): Progress in Industrial Mathematics at ECMI 2021. ECMI 2021. Mathematics in Industry, vol 39. Springer Cham / Virtual, Online, 2021, pp. 297–304; DOI: 10.1007/978-3-031-11818-0_39