SRS-Net: a universal framework for solving stimulated Raman scattering in nonlinear fiber-optic systems by physics-informed deep learning
Author:
Funder
National Natural Science Foundation of China
Beijing Nova Program
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s44172-024-00253-w.pdf
Reference45 articles.
1. Karniadakis, G. E. et al. Physics-informed machine learning. Nat. Revi. Phys. https://doi.org/10.1038/s42254-021-00314-5 (2021).
2. Tarantola, A. Inverse problem theory and methods for model parameter estimation. (SIAM, 2005).
3. Raissi, M., Yazdani, A. & Karniadakis, G. E. Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations. Science 367, 1026–1030 (2020).
4. Lu, L. et al. Physics-informed neural networks with hard constraints for inverse design. SIAM J. Sci. Comput. 43, B1105–B1132 (2021).
5. Raman, C. V. & Krishnan, K. S. A new type of secondary radiation. Nature 121, 501–502 (1928).
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