Prof. Steven Dufour | Fluid Mechanics | Best Researcher Award

Prof. Steven Dufour | Fluid Mechanics | Best Researcher Award

Polytechnique Montreal | Canada

Prof. Steven Dufour is a distinguished faculty member at École Polytechnique de Montréal with extensive international academic experience, including visiting professorships in the United States, Saudi Arabia, and Brazil. He is an active member of IEEE, SIAM, and PMI, and has led numerous research projects in computational methods and fluid mechanics, supervising a large number of researchers across different levels. His projects span areas such as scientific machine learning, quantum computing, wireless power transfer, magnetohydrodynamics, turbulence modeling, superconductivity, artificial neural networks, numerical methods, and optimization for engineering systems. He has taught a wide range of mathematical and engineering courses, including finite element methods, deep learning mathematics, linear algebra, and scientific computing, and has coordinated major undergraduate programs. Prof. Dufour also plays a strong leadership role in university governance, contributing significantly to academic councils, program development, labor agreement negotiations, and institutional committees, demonstrating consistent commitment to advancing engineering education and research.

Khademi, A., & Dufour, S. (2025). Physics-informed neural networks with trainable sinusoidal activation functions for approximating the solutions of the Navier–Stokes equations. Computer Physics Communications.

Khademi, A., Salari, E., & Dufour, S. (2025). Simulation of 3D turbulent flows using a discretized generative model physics-informed neural networks. International Journal of Non-Linear Mechanics, 170, 104988.

Khademi, A., & Dufour, S. (2024). A novel discretized physics-informed neural network model applied to the Navier–Stokes equations. Physica Scripta, 99(7), 076016.

Arab, H., Wang, D., Wu, K., & Dufour, S. (2022). A full-wave discontinuous Galerkin time-domain finite element method for electromagnetic field mode analysis. IEEE Access, 10, 125243–125253.

Arab, H., Arabsalmanabadi, B., & Dufour, S. (2022). A novel time-domain numerical methodology for the electromagnetic analysis of an H-plane tee power divider. The Journal of Engineering, 2022(10), 1032–1036.

Dr. Xu Liu | Fluid Cross-Sectional Velocity Field | Best Researcher Award

Dr. Xu Liu | Fluid Cross-Sectional Velocity Field | Best Researcher Award

Research Institute of Highway Ministry of Transport | China

Dr. Xu Liu is an Assistant Researcher at the Research Institute of Highway Ministry of Transport with extensive experience in multiphase flow detection, computational fluid dynamics (CFD), structural mechanics simulation, and precision sensor development. He has actively contributed to multiple National Natural Science Foundation of China (NSFC) projects and provincial innovation initiatives, focusing on gas–liquid and liquid–solid two-phase flow measurement, intelligent sensing systems, and metrological assurance in smart city applications. Xu Liu has published several high-impact SCI and Scopus-indexed papers on flow sensors, velocity field reconstruction, and turbulence modeling, integrating intelligent algorithms, CFD simulations, and sensor optimization to address key challenges in industrial flow monitoring. His work emphasizes practical engineering applications, combining theoretical modeling, experimental techniques, and data-driven approaches to enhance flow measurement accuracy and reliability.

Profile: Orcid 

Featured Publications

Liu, X., Song, Y., Zhao, D., Lan, K., Zhai, K., Wang, M., & Fang, L. (2025). Study on velocity profile of gas–liquid two-phase stratified flow in pipelines based on transfer component analysis–back propagation neural network. Physics of Fluids.