Bibliographic Details
| Title: |
Artificial Intelligence and Computational Issues in Engineering Applications. |
| Authors: |
Grabowska, Karolina, Krzywanski, Jaroslaw, Sosnowski, Marcin, Skrobek, Dorian |
| Source: |
Entropy; Jan2023, Vol. 25 Issue 1, p5, 4p |
| Subject Terms: |
COMPUTATIONAL intelligence, ARTIFICIAL intelligence, DEEP learning, ENGINEERING, REINFORCEMENT learning, FLUIDIZED-bed combustion, CURVE fitting, MASS transfer |
| Abstract: |
The experimental results presented in the paper and achieved using real datasets from Shanghai Telecom indicate that DQN-ESPA outperforms state-of-the-art algorithms such as the simulated annealing placement algorithm, Top-K placement algorithm, K-Means placement algorithm, and random placement algorithm. High-performance supercomputers and emerging computing clusters created in research and development centres are rapidly increasing available computing power, which scientists are eager to use to implement increasingly advanced computing methods [[1]]. Thus, computationally demanding artificial intelligence algorithms and computational fluid dynamics methods are used more widely to consider complex engineering issues and verify and provide new information on entropy or information theory concepts [[2]]. As can be seen above, the original research articles, as well as review articles focused on optimization by artificial intelligence (AI) algorithms on computational and entropy issues, have been submitted to the Special Issue. [Extracted from the article] |
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| Database: |
Complementary Index |
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