Filippos Sofos

University of Thessaly — Greece

Engineering · Physical Sciences

22h-index1.5kcitations78works3.92yr avg
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Research Topics

Nanopore and Nanochannel Transport Studies(28), Fluid Dynamics and Turbulent Flows(15), Machine Learning in Materials Science(13), Phase Equilibria and Thermodynamics(10), Advanced Image Processing Techniques(8)

Publications78 total

Combined machine learning approaches to predict thermal conductivity for liquid mixtures
Springer Link (Chiba Institute of Technology)·2026Open Access
Combined machine learning approaches to predict thermal conductivity for liquid mixtures
Science and Technology for Energy Transition·2026Open Access
Machine Learning-Enhanced Molecular Dynamics: Current State, Challenges and Perspectives
Archives of Computational Methods in Engineering·2026Open Access
Prediction of neutral gas pressure in Wendelstein 7-X: Statistical analysis and machine learning
Physics of Plasmas·2026Open Access
Combined machine learning approaches to predict thermalconductivity for liquid mixtures
2025
Advancing Fluid Mechanics with Artificial Intelligence and Machine Learning
Fluids·2025Open Access
Turbulent channel flow: A physics-informed neural network approach with embedded parameter optimization
Physics of Fluids·2025· 2 cited
Reconstructing turbulence: A deep learning–enhanced interpolation approach
Physica D Nonlinear Phenomena·2025· 2 citedOpen Access
Data-driven investigation of analytical relationships in moiré structured materials
Computational Materials Science·2025Open Access
Physically Consistent Self-Diffusion Coefficient Calculation with Molecular Dynamics and Symbolic Regression
International Journal of Molecular Sciences·2025· 1 citedOpen Access
Data driven prediction of the neutral gas pressure in the stellarator Wendelstein 7-X
Plasma Physics and Controlled Fusion·2025· 1 citedOpen Access
A review of deep learning for super-resolution in fluid flows
Physics of Fluids·2025· 10 cited
Advancing super-resolution of turbulent velocity fields: An artificial intelligence approach
Physics of Fluids·2025· 5 cited
Spatiotemporal super-resolution forecasting of high-speed turbulent flows
Physics of Fluids·2025· 11 cited
Enhancing indoor temperature mapping: High-resolution insights through deep learning and computational fluid dynamics
Physics of Fluids·2025· 5 cited
Refining Flow Structures with Deep Learning and Super Resolution Methods
2024Open Access
Turbulent Micropolar Open-Channel Flow
Fluids·2024· 1 citedOpen Access
Comparison of super-resolution deep learning models for flow imaging
Computers & Fluids·2024· 7 cited
Editorial: Nanofluidics: computational methods and applications
Frontiers in Nanotechnology·2024Open Access
Ultra-scaled deep learning temperature reconstruction in turbulent airflow ventilation
Physics of Fluids·2024· 9 cited
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Frequent Co-authors

Theodoros E. Karakasidis(32), A. Liakopoulos(17), Dimitris Drikakis(10), Ioannis W. Kokkinakis(8), Konstantinos Papastamatiou(4), U. S. Mahabaleshwar(4), Dimitrios Angelis(3), A.E. Giannakopoulos(3), S. Michael Spottswood(3), R. Mahesh(3), Christos Stavrogiannis(2), Maria Sagri(2), Ioannis E. Sarris(2), Efstathios Chatzoglou(2), Kalliopi K. Exarchou-Kouveli(1), Daniel Akabua(1), George Charilas(1), Dimitrios Spetsiotis(1), Avraam Charakopoulos(1), T. Maranna(1)