Guillermo Suarez, Chair for Scientific Computing (SciComp), TU Kaiserslautern
Title: Non-Linear Surrogate Model Design for Aerodynamic Dataset Generation
One of the key components for designing an Air Vehicle is an Aerodynamic Dataset – a model representing the aircraft’s aerodynamic behavior throughout the entire flight envelope. One of the major benefits of Artificial Intelligence (AI) is the possibility to automate most parts of a design process in a highly reduced timeframe. It is therefore believed that one or several AI techniques show potential for automating parts of the current dataset generation process.
In this talk we will present the latest work regarding the design and development of a non-linear surrogate model to adequately predict static stability and control parameters for the Unmanned Combat Air Vehicle DLR-F17 and the DLR-F19 aircraft. Within this scope, two different topologically optimized machine learning based surrogate modelling techniques are investigated in order to achieve the best generalization properties.
The first architecture being investigated is Artificial Neural Networks (ANN), who have gained great popularity due to their ability to efficiently process large amounts of data. The second architecture to be investigated relies in Ensemble Learning (EL) methods. For the current work three different ensemble learning models have been assessed: Random Forests, Adaptive Boosting and Extreme Gradient Boosting.
Additional lines of research also investigate machine learning algorithms with multiple outputs in order to explore whether a multi-output learning framework brings benefits in the form of increased predictive performance compared to a framework based on training multiple disjoint models. Furthermore, because of their outstanding performance in feature extraction, 1-dimensional Convolutional Neural Networks (CNN) will be also considered as a surrogate model.
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