基于物理嵌入神经网络的叶栅损失模型

Yunyang Feng; Xizhen Song; Wei Yuan; Hanan Lu

doi:10.13224/j.cnki.jasp.20220750

基于物理嵌入神经网络的叶栅损失模型

Yunyang Feng
, Xizhen Song
, Wei Yuan^*
, Hanan Lu

^*此作品的通讯作者

能源与动力工程学院

Beihang University

科研成果: 期刊稿件 › 文章 › 同行评审

5 引用（Scopus）

摘要

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.

投稿的翻译标题	Physics-informed neural networks based cascade loss model
源语言	繁体中文
页（从-至）	845-855
页数	11
期刊	Hangkong Dongli Xuebao/Journal of Aerospace Power
卷	38
期	7
DOI	https://doi.org/10.13224/j.cnki.jasp.20220750
出版状态	已出版 - 7月 2023

关键词

deep learning
loss prediction
neural networks
physics-informed
surrogate model

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10.13224/j.cnki.jasp.20220750

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引用此

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AU - Song, Xizhen

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AU - Lu, Hanan

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AB - 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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KW - loss prediction

KW - neural networks

KW - physics-informed

KW - surrogate model

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JF - Hangkong Dongli Xuebao/Journal of Aerospace Power

IS - 7

ER -

基于物理嵌入神经网络的叶栅损失模型

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关键词

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