Academic Level: Associate Professor
Scientific area: Condensed Matter Physics with Computational Simulation techniques
Download : [Curriculum Vitae (EN)]
| Personal Site | https://www.phys.uth.gr/fsofos |
| fsofos [at] uth.gr | |
| Phone Num. (Work) | (+30)2231060139 |
| Office | |
| Hours of Collaboration with Students | Wednesday/Thursday 12-14.00 |
- Machine Learning, Deep Learning in Condensed Matter Physics and Fluid Mechanics
- Explainable/Transparent Artificial Intelligence
- Fluid transport properties
- Nano/Micro-fluidics
- Multiscale particle simulations (Molecular Dynamics, Smoothed-Particle Hydrodynamics)
- 2022 Best paper award: F. Sofos, C. Stavrogiannis, K.K. Exarhou-Kouveli, D. Akaboua, G. Charilas, T.E. Karakasidis, Current Trends in Fluid Research in the era of Artificial Intelligence: A Review, Fluids 7 (2022) 116.
- A. Palasis, G. Sofiadis, F. Sofos, A. Liakopoulos, Turbulent channel flow: A physics-informed neural network approach with embedded parameter optimization, Physics of Fluids 37 (11) (2025)
- D. Angelis, F. Sofos, S. Misdanitis, C. Dritselis, T.E. Karakasidis, D. Valougeorgis, V. Haak, D. Naujoks, G. Schlisio, S.A. Bozhenkov, V. Perseo and W7-X Team, Data driven prediction of the neutral gas pressure in the stellarator Wendelstein 7-X, Plasma Physics and Controlled Fusion 67, 075004 (2025)
- D. Angelis, F. Sofos, T.E. Karakasidis, Reassessing the transport properties of fluids: A symbolic regression approach, Physical Review E 109, 015105 (2024).
- F. Sofos, D. Drikakis, I.W. Kokkinakis, Deep learning architecture for sparse and noisy turbulent flow data, Physics of Fluids 36 (3), 035155 (2024).
- F. Sofos, E. Rouka, V. Triantafyllia, E. Andreakos, K.I. Gourgoulianis, E. Karakasidis, T. Karakasidis, Development and validation of a symbolic regression-based machine learning method to predict COVID-19 in-hospital mortality among vaccinated patients. Health and Technology (2024). https://doi.org/10.1007/s12553-024-00886-z
- D. Angelis, F. Sofos, T.E. Karakasidis, Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives. Archives of Computational Methods in Engineering 30, 3845–3865 (2023).
