Simulation of multi-species flow and heat transfer using physics-informed neural networks

In the present work, single- and segregated-network PINN architectures are applied to predict momentum, species and temperature distributions of a dry air humidification problem in a simple 2D rectangular domain. The created PINN models account for variable fluid properties, species- and heat-diffusion and convection. Both the mentioned PINN architectures were trained using different hyperparameter settings, such as network width and depth to...