Flow temperature field reconstruction based on physics-informed neural network

Flow temperature fields are more challenging to measure than the velocity fields. Accurate reconstruction of temperature distributions from the velocity data is crucial in heat transfer problems, especially for experimental data analysis. We apply a physics-informed neural network (PINN) to accurately infer temperature fields at arbitrary space-time points within the measurement domain, using only the velocity data and initial temperature field. The PINN minimizes a composite loss function that incorporates residuals from both the Navier–Stokes and energy equations. We validate this approach using three representative two-dimensional heat transfer cases: steady flow around a heated cylinder, synthetic jet impinging on a heated wall, and Rayleigh–Bénard convection. Recognizing that the energy and momentum equations are decoupled under incompressible conditions, we introduce an alternative network architecture in which the temperature is predicted via a dedicated subnetwork. This modification improves reconstruction accuracy by approximately 10%.