EPJ Web Conf.
Volume 247, 2021PHYSOR2020 – International Conference on Physics of Reactors: Transition to a Scalable Nuclear Future
|Number of page(s)||8|
|Published online||22 February 2021|
- J. Sirignano and S. Konstantinos, “DGM: A deep learning algorithm for solving partial differential equations.” Journal of Computational Physics 375, pp. 1339–1364 (2018). [Google Scholar]
- J. Han, A. Jentzen and E. Weinan, “Solving high-dimensional partial differential equations using deep learning,” Proceedings of the National Academy of Sciences, 115(34), pp.8505–8510 (2018). [Google Scholar]
- D. Finol, L. Yan, M. Vijay and S. Ankit, “Deep convolutional neural networks for eigenvalue problems in mechanics,” International Journal for Numerical Methods in Engineering, 118(5), pp 258–275 (2019). [Google Scholar]
- J. Ling, K. Andrew and T. Jeremy, “Reynolds averaged turbulence modelling using deep neural networks with embedded invariance,” Journal of Fluid Mechanics, 807, pp 155–166 (2016). [Google Scholar]
- A. Saeed, et al. “Novel fault diagnosis scheme utilizing deep learning networks,” Progress in Nuclear Energy, 118,103066 (2020). [Google Scholar]
- H. G. Kim, H. C. Soon, and H. L. Byung, “Pressurized water reactor core parameter prediction using an artificial neural network,” Nuclear Science and Engineering, 113(1), pp 70–76 (1993). [Google Scholar]
- M. G. Lysenko, H. I. Wong and G. I. Maldonado. “Neural network and perturbation theory hybrid models for eigenvalue prediction,” Nuclear science and engineering, 132(1), pp 78–89 (1999). [Google Scholar]
- Q. Zhang, A deep learning model for solving the eigenvalue of the diffusion problem of 2-D reactor core, Proceedings of the Reactor Physics Asia 2019, Osaka, Japan (2019). [Google Scholar]
- K. Simonyan and Z. Andrew, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv: 1409, pp 1556 (2014). [Google Scholar]
- J. Long, E. Shelhamer and T. Darrell. Fully convolutional networks for semantic segmentation, pp. 3431–3440, In Proceedings of the IEEE conference on computer vision and pattern recognition (2015). [Google Scholar]
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