Numerical Simulation of Transient Heat Conduction With Moving Heat Source Using Physics Informed Neural Networks

Abstract

In this article, the physics informed neural networks (PINNs) is employed for the numerical simulation of heat transfer involving a moving source under mixed boundary conditions. To reduce computational effort and increase accuracy, a new training method is proposed that uses a continuous time-stepping through transfer learning. A single network is initialized and used as a sliding window function across the time domain. On this single network each time interval is trained with the initial condition for (n+1)th iteration as the solution obtained at nth iteration. Thus, this framework enables the computation of large temporal intervals without increasing the complexity of the network itself. The proposed framework is used to estimate the temperature distribution in a homogeneous medium with a moving heat source. The results from the proposed framework is compared with traditional finite element method and a good agreement is seen.

Keywords

boundary value problem / Gaussian source / moving heat source / neural network / new training method / PINNs / transfer learning / transient heat conduction

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2025 The Author(s). International Journal of Mechanical System Dynamics published by John Wiley & Sons Australia, Ltd on behalf of Nanjing University of Science and Technology.