REFERENCES

1. Jiang, H.; Liu, J.; Wang, Z.; et al. Assessment of spatial and temporal TEC variations derived from ionospheric models over the polar regions. J. Geod. 2019, 93, 455-71.

2. Liu, L.; Zou, S.; Yao, Y.; Wang, Z. Forecasting global ionospheric TEC using deep learning approach. Space. Weather. 2020, 18, e2020SW002501.

3. Tang, J.; Li, Y.; Yang, D.; Ding, M. An approach for predicting global ionospheric TEC using machine learning. Remote. Sens. 2022, 14, 1585.

4. Cho, K.; van, Merrienboer., B.; Gulcehre, C.; et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv 2014; arXiv:1406.1078. https://doi.org/10.48550/arXiv.1406.1078. (accessed 20 May 2025).

5. Hochreiter, S.; Schmidhuber, J. Long short-term memory. Neural. Comput. 1997, 9, 1735-80.

6. Yu, Y.; Si, X.; Hu, C.; Zhang, J. A review of recurrent neural networks: LSTM cells and network architectures. Neural. Comput. 2019, 31, 1235-70.

7. Sun, W.; Xu, L.; Huang, X.; et al. Forecasting of ionospheric vertical total electron content (TEC) using LSTM networks. In: 2017 International Conference on Machine Learning and Cybernetics (ICMLC), Ningbo, China. Jul 09-12, 2017. IEEE; 2017. pp. 340-4.

8. Chen, Z.; Liao, W.; Li, H.; Wang, J.; Deng, X.; Hong, S. Prediction of global ionosphere TEC base on deep learning. ESS. Open. Archive. 2021.

9. Chen, Z.; Wang, K.; Li, H.; et al. Storm-time characteristics of ionospheric model (MSAP) based on multi-algorithm fusion. Space. Weather. 2024, 22, e2022SW003360.

10. Liu, L.; Morton, Y. J.; Liu, Y. ML prediction of global ionospheric TEC maps. Space. Weather. 2022, 20, e2022SW003135.

11. Xia, G.; Zhang, F.; Wang, C.; Zhou, C. ED-ConvLSTM: a novel global ionospheric total electron content medium-term forecast model. Space. Weather. 2022, 20, e2021SW002959.

12. Ren, X.; Zhao, B.; Ren, Z.; Wang, Y.; Xiong, B. Deep learning-based prediction of global ionospheric TEC during storm periods: mixed CNN-BiLSTM method. Space. Weather. 2024, 22, e2024SW003877.

13. Raissi, M.; Perdikaris, P.; Karniadakis, G. E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 2019, 378, 686-707.

14. Long, Z.; Lu, Y.; Dong, B. PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network. J. Comput. Phys. 2019, 399, 108925.

15. Champion, K.; Lusch, B.; Kutz, J. N.; Brunton, S. L. Data-driven discovery of coordinates and governing equations. Proc. Natl. Acad. Sci. U. S. A. 2019, 116, 22445-51.

16. Chen, Y.; Luo, Y.; Liu, Q.; Xu, H.; Zhang, D. Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE). Phys. Rev. Res. 2022, 4, 023174.

17. Yazdani, S.; Tahani, M. Data-driven discovery of turbulent flow equations using physics-informed neural networks. Phys. Fluids. 2024, 36, 035107.

Intelligence & Robotics
ISSN 2770-3541 (Online)

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles are preserved here permanently:

https://www.portico.org/publishers/oae/