Constantinos Siettos
Associate Professor
"If there were no difference between essence and appearance, there would be no need for science"
Constantinos Siettos works in the areas of Numerical Analysis, Scientifc Machine Learning and Scientific Computing for the bridging of the scales, modelling, bifurcation analysis and control of complex large scale and multiscale dynamical systems. The scientific keystones of the research integrate Numerical Analysis, Machine Learning and Manifold Learning, Data-Mining, Complex Systems, Nonlinear Systems, Bifurcation theory, and Control theory. There are three main directions in this effort:
(A) Development of "next generation" Equation-free numerical analysis methods that bypass the construction of surrogate Machine Learning models that impose bias in the numerical analysis. The framework is based on manifold learning/ data mining for dealing with the curse of dimensionality, thus discovering latent spaces where the emergent dynamics evolve. The aim is to bridge the scales of multiscale/complex/stochastic dynamical systems for their rigorous numerical analysis.
(B) Development of (physics-informed) machine learning and manifold learning algorithms for the numerical solution of both the inverse problem (the discovery of physical laws in the form of ODEs and PDEs operators from big data generated by high fidelity dynamical models) and the forward problem (the numerical solution of differential equations).
(C) Modelling of complex multiscale and stochastic systems
Using state-of-the-art numerical simulation methods, and agent-based models, I strive to address emerging interdisciplinary challenging complex problems in Computational Epidemiology, Computational Neuroscience, Crowd and Social Dynamics, Economics and Finance, Environmental Modelling, in collaboration with strong and interested colleagues.
firstname.lastname at unina dot the alpha-2 country code
Recent Publications
Featured Article in Nature Communications Article awarded with the SISC Featured Article in Chaos
in AI, Physics and Applied Mathematics Reproducibility Badge: code and data available
News
FEATURED ARTICLE IN NATURE COMMUNICATIONS!
Fields: AI and Machine Learning & Applied Physics and Mathematics
International Conference "Scientific Machine Learning, emerging topics", Trieste, SISSA, Italy, 18-21 June 2024.
2nd Conference of the Italian Chapter of the Complex Systems Society: Risk and Complexity, Naples, Italy, 9-11 October 2023
21st Summer Meeting in Risk Finance and Stochastics, 9 - 13 September 2024, Athens University of Economic and Business, Greece
30th Summer School and Conference on Dynamic Systems & Complexity , Calandra University Camping, Halkidiki, Greece, 28/8/2024 – 6/9/2024.