Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks
This work addresses computational efficiency in heat transfer simulations for engineering applications, but it is incremental as it builds on existing PINN methods with a specific training improvement.
The paper tackles the problem of simulating transient heat conduction with a moving heat source by proposing a new training method for Physics Informed Neural Networks (PINNs) that uses continuous time-stepping through transfer learning to reduce computational effort, achieving results that show good agreement with traditional finite element methods.
In this paper, the physics informed neural networks (PINNs) is employed for the numerical simulation of heat transfer involving a moving source. To reduce the computational effort, a new training method is proposed that uses a continuous time-stepping through transfer learning. Within this, the time interval is divided into smaller intervals and a single network is initialized. On this single network each time interval is trained with the initial condition for (n+1)th as the solution obtained at nth time increment. 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.