| Citation: | FENG Yunyang, SONG Xizhen, YUAN Wei, et al. Physics-informed neural networks based cascade loss model[J]. Journal of Aerospace Power, 2023, 38（7）：1615-1625 doi: 10.13224/j.cnki.jasp.20220750
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It is difficult to modify and broaden the scope of application for empirical models because of its inadequate ability to fit strong nonlinear function relationship. In order to solve these problems, a physics-informed deep learning cascade loss model of embedding the pressure distribution of cascade into neural networks was proposed. The loss prediction error decreased by 22.3% compared with empirical model for end-to-end neural networks and 37.9% compared with physics-informed model.
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