Accurate solutions to real-time complex nuclear engineering problems require high computational resources. Artificial Intelligence proven effective in various engineering problems is a promising method for analysis of heat transfer problems in nuclear engineering. This research uses a deep learning approach of artificial intelligence to study the heat transfer behavior of the lithium film flow on the plasma-facing side of the Tokamak divertor. A Multi-Domain Physics Informed Neural Network (MD-PINN) model was developed to solve the steady-state convection-diffusion partial differential equation leveraging automatic differentiation to compute the loss function from the residuals of the equation. The model was validated with analytical solutions for two-layer two materials' 1D and 2D heat conduction problems. The model was improved through parametric analysis for convergence rate and accuracy. Steady-state temperature distribution was obtained using the MD-PINN model and compared with the reference results. A good agreement of the results shows the capability of the MD-PINN as an alternative to the numerical simulation.

Artificial Intelligence,heat transfer,Tokamak divertor,Physics Informed Neural Network,differential equation