نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشگاه قم، شهر قم ، ایران
2 بخش مهندسی مکانیک، دانشکده مهندسی دانشگاه قم
3 دانشگاه قم
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
Due to the high time and computational cost of the direct or numerical solution methods of the partial differential equations governing phenomena, methods based on deep learning algorithms are commonly used in modeling phenomena. In this research, to simulate temperature changes in a two-dimensional field, three different methods based on deep learning algorithms have been used, including a model based on convolutional neural networks, model based on convolutional kernel containing the desired phenomenon pattern and a reduced-dimension Model using autoencoder method. The results of two-dimensional equilibrium temperature distribution simulations using the three methods were compared with the results obtained from the finite difference method. The results showed that the Reduced-Dimension Model (with an accuracy of 2.528×10-6 °C) has higher accuracy compared to the other two models. While the kernel-based model, is superior to the other two models in terms of steady-state temperature data consumption (only 400 data of size 8 × 8).
کلیدواژهها [English]
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