A novel approach to model design and tuning through automatic parameter screening and optimization theory and application to a helicopter flight simulator case-study

The aim of this paper is to describe a novel methodology for model-design and tuning in computer simulations, based on automatic parameter screening and optimization. Simulation requires three steps: mathematical modelling, numerical solution, and tuning of the model's parameters. We address Tuning because, at the state-of-the-art, the development of life-critical simulations requires months to appropriately tune the model. Our methodology can be split in Screening (identification of the relevant parameters to simulate a system) and Optimization (search of optimal values for those parameters). All techniques are fully general, because they leverage ideas from Machine-Learning and Optimization Theory to achieve their goals without directly analysing the simulator's mathematical model. Concerning screening, we show how Machine-Learning algorithms, based on Neural Networks and Logistic Regression, can be used for ranking the parameters according to their relevance. Concerning optimization, we describe two algorithms: an adaptive hill-climbing procedure and a novel strategy, specific for model tuning, called sequential masking. Eventually, we show the performances achieved and the impact on the time and effort required for tuning a helicopter flight-simulator, proving that the proposed techniques can significantly speed-up the process.