On the Use of Supervised Learning Techniques to Speed up the Design of Aeronautics Components
published: July 20, 2009, recorded: July 2009, views: 187
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A crucial issue in the design of complex systems is the evaluation of a large number of potential alternative designs. A too expensive evaluation procedure can consequently slow down the search for good configurations mainly in the case of high dimensional parameter spaces. The talk will discuss the use of machine learning techniques for speeding up the evaluation and the exploration of large design spaces. In particular, two supervised learning techniques, feedforward neural networks and lazy learning, are assessed and compared in the task of accelerating the design of a heat-pipe, a cooling device commonly used in aeronautics and electronics.
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