Exemplar-based objective classification of gust-induced loads across multiple flight conditions

arXiv:2608.12448v1 Announce Type: new Abstract: 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.

Related

• An Objective Performance Evaluation of the LSTM Networks in Time Series Classification
• Low-Cost High-Order Singular Value Decomposition for Tensor-Based Reconstruction from Sparse Sensor Measurements: Urban Flow and Air-Quality Applications
• A Causal DAG Prior for Synthetic Time-Series Classification Datasets
• A Systematic Evaluation of Imbalance Handling Methods in Biomedical Binary Classification

Source: arXiv cs.LG | 2026-08-14