Heat Transfer Modelling with Physics-Informed Neural Network (PINN)
The numerical simulations of partial differential equations aid us in studying the nanofluid flow in the porous media, the analysis of the dispersion of pollutants, and many other physical phenomena. However, to simulate such phenomena requires tremendous computational power, and it increases with t...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Published: |
Springer Science and Business Media Deutschland GmbH
2022
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| Online Access: | http://scholars.utp.edu.my/id/eprint/34088/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140231783&doi=10.1007%2f978-3-031-04028-3_3&partnerID=40&md5=6dda30d6c6fb1d7efa59ee36502b4c34 |
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| Summary: | The numerical simulations of partial differential equations aid us in studying the nanofluid flow in the porous media, the analysis of the dispersion of pollutants, and many other physical phenomena. However, to simulate such phenomena requires tremendous computational power, and it increases with the number of parameters. In this chapter, we will explore the application of the Physics-Informed Neural Network (PINN) in solving heat equation with distinct types of materials. To leverage the GPU performance and cloud computing, we perform the simulations on the Google Cloud Platform. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
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