轨迹设计通过无监督概率学习在最优流形-勘误表

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

DataCentric Engineering Pub Date : 2022-09-14 DOI:10.1017/dce.2022.30

C. Safta, R. Ghanem, M. J. Grant, Michael J. Sparapany, H. Najm

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引用次数: 0

摘要

“行星再入轨迹的实时优化是一项艰巨的任务，需要同时考虑与飞行动力学、飞行过程中的飞行器限制、可变的初始和最终条件以及这些系统所用模型的高维参数集有关的约束。”（p.1）其次，第2页下句中的“高超音速问题”改为“行星再入问题”：

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Trajectory design via unsupervised probabilistic learning on optimal manifolds – Corrigendum

“Real-time optimization of planetary reentry trajectories is a difficult task that requires simultaneous accounting for constraints related to flight dynamics, vehicle limitations during flight, variable initial and terminal conditions, and a high-dimensional parameter set for the models employed for these systems.” (p.1) Secondly, the phrase “hypersonic problems” in the following sentence on p.2 is corrected with the phrase “planetary reentry problems”:

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来源期刊

DataCentric Engineering Engineering-General Engineering

CiteScore

5.60

自引率

0.00%

发文量

审稿时长

12 weeks