Filippos Sofos

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Assistant Professor

University of Thessaly

Greece

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Professors in Fluid Mechanics

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About

Filippos Sofos is an Assistant Professor at the University of Thessaly, Greece. His research focuses on the application of deep learning and computational fluid dynamics to address challenges in turbulence and super-resolution in fluid flows. Recent articles include works on predicting gas pressure in stellarators and enhancing indoor temperature mapping using advanced AI techniques.

Recent Grants

Grant: Close

Multiscale modelling of environmental and free surface flows with particle-based methods (MOVEFREE)

Open Date: 2022-04-01

Close Date: 2025-03-01

Grant: Close

A Computational frAmework for MIcroscopic calculations and macroscopic predictions with NOvel machine learning methodS (CAMINOS)

Open Date: 2021-12-01

Close Date: 2023-11-01

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2022

Collaborators (3)

Theodoros E. Karakasidis

University of Thessaly

GREECE

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Dimitris Drikakis

CYPRUS

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Christos Papakonstantinou

Associate Professor

University of Thessaly

GREECE

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Filippos Sofos

Research Interests

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About

Recent Grants

Articles (27)

Reconstructing turbulence: A deep learning–enhanced interpolation approach

Data driven prediction of the neutral gas pressure in the stellarator Wendelstein 7-X

Physically Consistent Self-Diffusion Coefficient Calculation with Molecular Dynamics and Symbolic Regression

A review of deep learning for super-resolution in fluid flows

Advancing super-resolution of turbulent velocity fields: An artificial intelligence approach

Enhancing indoor temperature mapping: High-resolution insights through deep learning and computational fluid dynamics

Spatiotemporal super-resolution forecasting of high-speed turbulent flows

Comparison of super-resolution deep learning models for flow imaging

Turbulent Micropolar Open-Channel Flow

A hybrid molecular dynamics/machine learning framework to calculate the viscosity and thermal conductivity of Ar, Kr, Xe, O, and Ν

Ultra-scaled deep learning temperature reconstruction in turbulent airflow ventilation

From Sparse to Dense Representations in Open Channel Flow Images with Convolutional Neural Networks

Deep learning architecture for sparse and noisy turbulent flow data

A deep learning super-resolution model for turbulent image upscaling and its application to shock wave–boundary layer interaction

The application of data science and machine learning techniques in predicting the compressive strength of confined concrete

Reassessing the transport properties of fluids: A symbolic regression approach

Impact of an inclined magnetic field on couple stress fluid flow over a stretching surface with effect of Stefan blowing, radiation and chemical reaction

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

Fluid Properties Extraction in Confined Nanochannels with Molecular Dynamics and Symbolic Regression Methods

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

Smoothed Particle Hydrodynamics-Based Study of 3D Confined Microflows

Influence of carbon nanotube suspensions on Casson fluid flow over a permeable shrinking membrane: an analytical approach

Twofold Machine-Learning and Molecular Dynamics: A Computational Framework

Fiber-Reinforced Polymer Confined Concrete: Data-Driven Predictions of Compressive Strength Utilizing Machine Learning Techniques

Thermosolutal Marangoni Convection for Hybrid Nanofluid Models: An Analytical Approach

Effects of channel size, wall wettability, and electric field strength on ion removal from water in nanochannels

The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches

Collaborators (3)

Theodoros E. Karakasidis

Dimitris Drikakis

Christos Papakonstantinou

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