I am an Assistant Professor in the Department of Mathematics and Applied Mathematics of the University of Crete. I am also a member of the Institute of Applied and Computational Mathematics (IACM) of FORTH.
I Kičić, PR Vlachas, G Arampatzis, M Chatzimanolakis, L Guibas, P Koumoutsakos (2023) Adaptive learning of effective dynamics for online modeling of complex systems, Computer Methods in Applied Mechanics and Engineering 415, 116204.
L Amoudruz, A Economides, G Arampatzis, P Koumoutsakos (2023) The stress-free state of human erythrocytes: Data-driven inference of a transferable RBC model, Biophysical Journal 122 (8), 1517-1525.
PR Vlachas, G Arampatzis, C Uhler, P Koumoutsakos (2022) Multiscale simulations of complex systems by learning their effective dynamics, Nature Machine Intelligence 4 (4), 359-366.
N Biller-Andorno, A Ferrario, S Joebges, T Krones, F Massini, P Barth, G Arampatzis, M Krauthammer (2022) AI support for ethical decision-making around resuscitation: proceed with care, Journal of Medical Ethics 48 (3), 175-183.
SM Martin, D Wälchli, G Arampatzis, AE Economides, P Karnakov, P Koumoutsakos (2022) Korali: Efficient and scalable software framework for Bayesian uncertainty quantification and stochastic optimization, Computer Methods in Applied Mechanics and Engineering 389, 114264.
JM Rožanec, E Trajkova, J Lu, N Sarantinoudis, G Arampatzis, P Eirinakis, I Mourtos, M Onat, D Yilmaz, A Košmerlj, K Kenda, B Fortuna, D Mladenić (2021) Cyber-physical lpg debutanizer distillation columns: Machine-learning-based soft sensors for product quality monitoring, Applied Sciences 11 (24), 11790.
K Kalaboukas, J Rožanec, A Košmerlj, D Kiritsis, G Arampatzis (2021) Implementation of cognitive digital twins in connected and agile supply networks—An operational model, Applied Sciences 11 (9), 4103.
A Economides, G Arampatzis, D Alexeev, S Litvinov, L Amoudruz, L Kulakova, C Papadimitriou, P Koumoutsakos (2021) Hierarchical Bayesian uncertainty quantification for a model of the red blood cell, Physical Review Applied 15 (3), 034062.
I am interested in machine learning aided equation free methods for multiscale systems and machine learning for time-series prediction.
In addition I work on Bayesian uncertainty quantification, developing statistical models aimed at quantifying uncertainties encountered in parameter inference within high-performance computational models.
