Solving inverse problems with sparse noisy data, operator splitting and physics-constrained machine learning-Reference-Cited by-同舟云学术

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Solving inverse problems with sparse noisy data, operator splitting and physics-constrained machine learning

Published:2023-12-15 Issue:3 Volume:112 Page:2021-2036
ISSN:0924-090X
Container-title:Nonlinear Dynamics
language:en
Short-container-title:Nonlinear Dyn

Author:

Li Chun,Deng Lijun

Funder

Basic and Applied Basic Research Foundation of Guangdong Province

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Applied Mathematics,Mechanical Engineering,Ocean Engineering,Aerospace Engineering,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s11071-023-09127-w.pdf

Reference61 articles.

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2. Kobler, E., Effland, A., Kunisch, K., Pock, T.: Total deep variation for linear inverse problems. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7549–7558 (2020)

3. Chan, K.Y., Lam, H.-K., Jiang, H.: A genetic programming-based convolutional neural network for image quality evaluations. Neural Comput. Appl. 34(18), 15409–15427 (2022)

4. Li, C., Yang, Y., Liang, H., Wu, B.: Robust PCL discovery of data-driven mean-field game systems and control problems. IEEE Trans. Circ. Syst. I Regul. Pap. 68(6), 2651–2664 (2021)

5. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1–4), 259–268 (1992)

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