@article{scholars17487,
          volume = {444},
           title = {Heat Transfer Modelling with Physics-Informed Neural Network (PINN)},
             doi = {10.1007/978-3-031-04028-3{$_3$}},
           pages = {25--35},
            note = {cited By 0},
            year = {2022},
         journal = {Studies in Systems, Decision and Control},
       publisher = {Springer Science and Business Media Deutschland GmbH},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140231783&doi=10.1007\%2f978-3-031-04028-3\%5f3&partnerID=40&md5=6dda30d6c6fb1d7efa59ee36502b4c34},
        abstract = {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. {\^A}{\copyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.},
          author = {Dhamirah Mohamad, N. Z. and Yousif, A. and Shaari, N. A. B. and Mustafa, H. I. and Abdul Karim, S. A. and Shafie, A. and Izzatullah, M.},
            issn = {21984182}
}