Parametric Prediction of the Temperature Field in a Heat Conduction Problem with a Moving Heat Source, Using Physics-Informed Neural Networks without Labelled Data
DOI:
https://doi.org/10.70567/mc.v41i21.114Keywords:
PINN, Deep Learning, heat conduction, parametric solutionAbstract
In this work, we propose use physics-informed neural networks (PINN) to parametrically solve the governing equations of a two-dimensional heat conduction problem with a moving heat source. We obtain general solutions not only as a function of the space-and-time independent variables but also to a geometric parameter that controls the intensity of the thermal source, named the characteristic radius. The results show a precise fit with respect to the reference solutions obtained by the finite element method. Nevertheless, we report and analyze some failure modes linked with the accomplishment of the boundary conditions and also to those found as the heat source becomes more intense.
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