Free Access
| Issue |
Europhysics News
Volume 53, Number 2, 2022
Machine learning in physics
|
|
|---|---|---|
| Page(s) | 18 - 21 | |
| Section | Features | |
| DOI | https://doi.org/10.1051/epn/2022204 | |
| Published online | 27 April 2022 | |
- R. Bjork et al., J. Magn. Magn. Mater. 535, 168057 (2021) [CrossRef] [Google Scholar]
- K. He et al., Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), 770 (2016) [Google Scholar]
- A. Khan, V. Ghorbanian, and D. Lowther, IEEE Trans. Magn. 55-6, 7401305 (2019). [Google Scholar]
- S. Pollok, R. Bjork, and P.S. Jorgensen, IEEE Trans. Magn. 57-7, 2101604 (2021) [Google Scholar]
- A. Kovacs et al., J. Magn. Magn. Mater. 491, 165548 (2019) [CrossRef] [Google Scholar]
- H. Sasaki, and H. Igarashi, IEEE Trans. Magn. 55-6, 7401305 (2019) [Google Scholar]
- J. Degrave et al., Nature 602, 414 (2022) [CrossRef] [PubMed] [Google Scholar]
- S. Qi et al., IEEE J. Multiscale Multiphys. Comput. Tech. 5, 83 (2019) [Google Scholar]
- T.L. Gilbert, IEEE Trans. Magn. 40-6, 3443 (2004) [CrossRef] [Google Scholar]
- S. Pollok et al., “Magnetic Field Prediction Using Generative Adversarial Networks” (2022), arXiv:2203.07897. [Online]. Available: https://arxiv.org/abs/2203.07897 [Google Scholar]
- A. Kovacs et al., J. Magn. Magn. Mater. 548, 168951 (2022) [CrossRef] [Google Scholar]
- A. Beltran-Pulido, I. Bilionisb, and D. Aliprantisa, “Physicsinformed neural networks for solving parametric magnetostatic problems” (2022), arXiv:2202.04041. [Online]. Available: http://arxiv.org/abs/2202.04041 [Google Scholar]
- K.T. Schutt, O.T. Unke, and M. Gastegger, Proc. Int. Conf. Mach. Learn. (PMLR) 139, 9377 (2021) [Google Scholar]
- N. Geneva, and N. Zabaras, Neural Netw. 146, 272 (2022) [CrossRef] [Google Scholar]
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