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Unsteady aerodynamic prediction using limited samples based on transfer learning

Autor: Ji, Wen, Sun, Xueyuan, Li, Chunna, Jia, Xuyi, Wang, Gang, Gong, Chunlin

In this study, a method for predicting unsteady aerodynamic forces under different initial conditions using a limited number of samples based on transfer learning is proposed, aiming to avoid the need for large-scale high-fidelity aerodynamic simulat

Externí odkaz: http://arxiv.org/abs/2405.15470

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An accuracy-enhanced transonic flow prediction method fusing deep learning and reduced-order model

Autor: Jia, Xuyi, Gong, Chunlin, Ji, Wen, Li, Chunna

Publikováno v: Physics of Fluids,2024

It's difficult to accurately predict the flow with shock waves over an aircraft due to the flow's strongly nonlinear characteristics. In this study, we propose an accuracy-enhanced flow prediction method that fuses deep learning and reduced-order mod

Externí odkaz: http://arxiv.org/abs/2307.07702

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CNTS: Cooperative Network for Time Series

Autor: Yang, Jinsheng, Shao, Yuanhai, Li, ChunNa

The use of deep learning techniques in detecting anomalies in time series data has been an active area of research with a long history of development and a variety of approaches. In particular, reconstruction-based unsupervised anomaly detection meth

Externí odkaz: http://arxiv.org/abs/2302.09800

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Multistage Large Segment Imputation Framework Based on Deep Learning and Statistic Metrics

Autor: Yang, JinSheng, Shao, YuanHai, Li, ChunNa, Wang, Wensi

Missing value is a very common and unavoidable problem in sensors, and researchers have made numerous attempts for missing value imputation, particularly in deep learning models. However, for real sensor data, the specific data distribution and data

Externí odkaz: http://arxiv.org/abs/2209.11766

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Akademický článek

Unsteady aerodynamic modeling and flight trajectory simulation of dual-spin projectile based on DNN and transfer-learning

Autor: Ji, Wen ^a, Gong, Chunlin ^a, Jia, Xuyi ^a, Li, Chunna ^{a, ⁎}, Wang, Gang ^b

Publikováno v: In Aerospace Science and Technology December 2024 155 Part 3

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Akademický článek

Efficient approximating the most-probable-point trajectory method for time-variant reliability analysis

Autor: ZOU Nanzheng, GONG Chunlin, ZHANG Yunwei, MA Mengying, DU Siyi, LI Chunna

Publikováno v: Hangkong gongcheng jinzhan, Vol 15, Iss 1, Pp 51-60 (2024)

When solving complex time-variant reliability analysis（TRA）problems，the traditional TRA methods have the problem of low solving efficiency. Based on the approximating most-probable-points trajectory （AMPPT）method，the efficient approximati

Externí odkaz: https://doaj.org/article/04aabd47fceb4d0f8e82254cf82e6796

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