... information (in the form of pre-existing designs, e.g. optimized for various sets of performance specifications) into the machine learning framework involving data-driven (here, kriging interpolation) metamodels. These surrogates are used to render the initial point but also to produce an approximation...

... Multi-objective design Surrogate modeling Simulation-driven design Kriging interpolation Design trade-offs (2) α ( f ) = [ α 1 ( f )   …   α n − N ( f ) ] T = D 2 [ | x d v n ( 1 ) ( f ) |     …     | x d v n ( n − N...

... techniques include kriging interpolation (Simpson et al., 2001), radial basis function interpolation (Simpson et al., 2001), Gaussian process regression (Jacobs, 2016), neural networks (Zhang et al., 2018), support vector regression (Jacobs, 2012) and polynomial chaos expansion...

... 23 11 2016 29 11 2016 © Emerald Publishing Limited 2017 Emerald Publishing Limited Licensed re-use rights only Co-kriging Aerodynamic shape optimization Design space reduction Multi-objective design Variable-fidelity CFD models Kriging interpolation The development...