Numerical simulation of transient heat conduction with moving heat source using Physics Informed Neural Networks

Abstract

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.

References in corpus (10)

• Artificial Neural Networks for Solving Ordinary and Partial Differential Equations
• An Energy Approach to the Solution of Partial Differential Equations in Computational Mechanics via Machine Learning: Concepts, Implementation and Applications
• hp-VPINNs: Variational Physics-Informed Neural Networks With Domain Decomposition
• Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates
• A Review of automatic differentiation and its efficient implementation
• Variational Physics-Informed Neural Networks For Solving Partial Differential Equations
• Deep Learning in Deterministic Computational Mechanics
• Distributed physics informed neural network for data-efficient solution to partial differential equations
• SPINN: Sparse, Physics-based, and partially Interpretable Neural Networks for PDEs
• A deep learning framework for solution and discovery in solid mechanics