DeepGPLAEN aims to investigate the applicability of Deep Gaussian Process (DGP) as a surrogate model for stochastic design optimisation in aerospace engineering. In addition, DGP performance is going to be compared with traditional Gaussian Process (GP) performance when leading with non-stationary optimisation problems. The expected output of the project will be a optimisation open source toolbox for surrogate based aerodynamic design problems under uncertainty.
DeepGPLAEN: Deep Gaussian Process Learning for Aerospace Engineering
Peter Zeno Korondi
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2019-01-01
Abstract
DeepGPLAEN aims to investigate the applicability of Deep Gaussian Process (DGP) as a surrogate model for stochastic design optimisation in aerospace engineering. In addition, DGP performance is going to be compared with traditional Gaussian Process (GP) performance when leading with non-stationary optimisation problems. The expected output of the project will be a optimisation open source toolbox for surrogate based aerodynamic design problems under uncertainty.File in questo prodotto:
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