Polynomial Fitting Based on Integrable Deep Neural Networks for Landau-Energy of Ferroelectrics

Zhang Wenyu; Yan Yabin

Polynomial Fitting Based on Integrable Deep Neural Networks for Landau-Energy of Ferroelectrics

Zhang Wenyu, Yan Yabin

Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:421-429, 2024.

Abstract

Fitting the Landau-Energy polynomial has always been challenging because it is difﬁcult to directly obtain Landau-Energy data for coefﬁcient ﬁtting. One possible approach to address this problem is to handle the derivative of the Landau-Energy polynomial with respect to the second-order polar- ization (dielectric constant) to obtain relevant information about the Landau-Energy. This chapter will introduce a method based on integrable neural networks to obtain an approximate model for the Landau-Energy polynomial and its parameters.

Cite this Paper

BibTeX


@InProceedings{pmlr-v245-wenyu24a,
  title = 	 {Polynomial Fitting Based on Integrable Deep Neural Networks for Landau-Energy of Ferroelectrics},
  author =       {Wenyu, Zhang and Yabin, Yan},
  booktitle = 	 {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing},
  pages = 	 {421--429},
  year = 	 {2024},
  editor = 	 {Nianyin, Zeng and Pachori, Ram Bilas},
  volume = 	 {245},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {26--28 Apr},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v245/main/assets/wenyu24a/wenyu24a.pdf},
  url = 	 {https://proceedings.mlr.press/v245/wenyu24a.html},
  abstract = 	 {Fitting the Landau-Energy polynomial has always been challenging because it is difﬁcult to directly obtain Landau-Energy data for coefﬁcient ﬁtting. One possible approach to address this problem is to handle the derivative of the Landau-Energy polynomial with respect to the second-order polar- ization (dielectric constant) to obtain relevant information about the Landau-Energy. This chapter will introduce a method based on integrable neural networks to obtain an approximate model for the Landau-Energy polynomial and its parameters.}
}

Endnote

%0 Conference Paper
%T Polynomial Fitting Based on Integrable Deep Neural Networks for Landau-Energy of Ferroelectrics
%A Zhang Wenyu
%A Yan Yabin
%B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing
%C Proceedings of Machine Learning Research
%D 2024
%E Zeng Nianyin
%E Ram Bilas Pachori	
%F pmlr-v245-wenyu24a
%I PMLR
%P 421--429
%U https://proceedings.mlr.press/v245/wenyu24a.html
%V 245
%X Fitting the Landau-Energy polynomial has always been challenging because it is difﬁcult to directly obtain Landau-Energy data for coefﬁcient ﬁtting. One possible approach to address this problem is to handle the derivative of the Landau-Energy polynomial with respect to the second-order polar- ization (dielectric constant) to obtain relevant information about the Landau-Energy. This chapter will introduce a method based on integrable neural networks to obtain an approximate model for the Landau-Energy polynomial and its parameters.

APA


Wenyu, Z. & Yabin, Y.. (2024). Polynomial Fitting Based on Integrable Deep Neural Networks for Landau-Energy of Ferroelectrics. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:421-429 Available from https://proceedings.mlr.press/v245/wenyu24a.html.

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