用少量典型样本实现复杂风载的客观分类，提升可解释性。
Exemplar-based objective classification of gust-induced loads across multiple flight conditions
- 通过机器学习提取实验数据特征，筛选关键代表性样本
- 在6种飞行姿态下发现9类重复出现的响应模式
- 结果便于专家分析，适合航空载荷研究者参考
能否找到一种客观分类标准，对多种飞行条件下由阵风引发的气动载荷进行系统划分？同时保持像飞行姿态这类粗粒度参数标注一样的可解释性？本方法通过机器学习表征大量实验观测数据，并采用归纳策略选取最具代表性的最小样本集。这些典型样本构成基于相似性的客观分类依据，便于专家审查并指导后续精细实验。我们在一个包含3480个随机阵风作用下飞翼模型压力载荷测量值的数据集上验证该方法，覆盖六种飞行姿态。结果识别出九类在多个姿态中反复出现的基本响应类型；对某一类型瞬态响应的分析，有助于理解其背后的流体力学机制。
原文摘要 · Abstract (English)
Is it possible to find an objective classification criterion that organizes the complexity of gust-induced loads across many flight conditions? And one that remains as interpretable as a labelling based on coarse parameters, such as the flight attitude? Our approach encodes a large number of experimental observations through a machine-learned representation and applies a summarization procedure to select a minimal subset of highly significant exemplars. The exemplars provide a similarity-based objective classification criterion of all the observations, they can be more conveniently inspected by experts and can become subject of more refined experiments. We demonstrate the approach on a database of 3480 pressure-load measurements induced by random gusts on a flying-wing model across six flight attitudes. We find nine fundamental response types that recur across multiple attitudes; analysis of a type's transient response enables physical intuition into the underlying fluid mechanics.
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