Physics-Informed Neural Network Surrogate Modeling Approach of Active/Passive Flow Control for Drag Reduction
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8018-5_18
Reference20 articles.
1. Sun, G., Wang, S.: A review of the artificial neural network surrogate modeling in aerodynamic design. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. 233(16), 5863–5872 (2019)
2. Sun, L., Wang, J.X.: Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data. Theoretical Appl. Mech. Lett. 1, 10(3), 161–169 (2020)
3. Zhu, Q., Liu, Z., Yan, J.: Machine learning for metal additive manufacturing: predicting temperature and melt pool fluid dynamics using physics-informed neural networks. Comput. Mech.. Mech. 67, 619–635 (2021)
4. Forster, M., Feldman, J., Lyes, P., Johns, J., Warsop, C.: Surrogate modelling of active flow control. In: AIAA SCITECH 2023 Forum p. 2314 (2023)
5. Yondo, R., Bobrowski, K., Andrés, E., Valero, E.: A review of surrogate modeling techniques for aerodynamic analysis and optimization: current limitations and future challenges in industry. Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences, pp. 19–33 (2019)
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