, Our contributions are twofold: (i) we have proposed individual models trained on in-air data to improve the aeronautics performance of individual aircrafts, rather than industry-wide calibrated parameters. This allows in particular for the search of more efficient (e.g., flight duration, speed, fuel consumption, etc.) trajectories for aircrafts (ii) we have designed a generic framework combining off-the-shelf machine learning with domain-specific approximations, which can be used in any data-intensive engineering discipline. We certainly hope that this approach can be replicated in other fields of study
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URL : https://hal.archives-ouvertes.fr/hal-00650905
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