Dr. Yan Su is an Associate Professor in the Department of Electromechanical Engineering and Director of the Solar Energy Laboratory at the Faculty of Science and Technology, University of Macau. He received his Ph.D. in Mechanical Engineering from the University of Minnesota, USA, following an M.S. from the Hong Kong University of Science and Technology and a B.S. from Tsinghua University. Dr. Su's research focuses on thermal engineering, renewable energy, energy storage systems, lattice Boltzmann methods, porous media, computational heat and mass transfer, battery thermal management, and intelligent energy systems. His work integrates advanced numerical modeling, artificial intelligence, and optimization techniques to address challenges in sustainable energy and engineering applications. He has authored a Springer book, contributed book chapters, and published extensively in leading international journals, including Chemical Engineering Journal, International Journal of Heat and Mass Transfer, Renewable Energy, Applied Thermal Engineering, Physica A, and the Journal of Energy Storage. His research has significantly advanced mesoscale simulations, electrochemical transport, wind energy forecasting, microfluidics, and smart energy technologies.

Physics Informed Neural Networks for Nano Thermofluids A physics-informed neural network (PINN) framework for efficient prediction of nano particle thermofluid flow is built including a new inverse dynamic prediction scheme of governing parameter sets. Two PINN models based on physics-guided loss functions formulated by governing equation residuals, the Navier-Stokes equations informed neural network (NS-NN) and the Reynolds averaged Navier-Stokes equations informed neural network (RANS-NN), are presented. A standard four-module computational code is developed in C++ for comparative studies. Code validations for theoretical vortex flow fields show that the prediction errors are less than 1%, 1%, and 5% for outputs, first and second gradients, respectively. Inverse parameter sets are obtained automatically and dynamically with a root means square control mechanics for various physics governing parameters simultaneously.   Both transient flow and temperature fields for natural convective nanolfuid flow in a magnetic field are predicted with high accuracy. Moreover, enhancement of the instability is observed with the help of the PINN compared to some special cases of ground truth data.   Results show that the PINN with inverse dynamical parameters can achieve faster convergence speed and higher accuracy compared to conventional NNs.