361 citations · 518 across the 7 of their papers we have counts for
4 papers · 1 filter
SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes
Paul Saves, Remi Lafage, Nathalie Bartoli +6
The Surrogate Modeling Toolbox (SMT) is an open-source Python package that offers a collection of surrogate modeling methods, sampling techniques, and a set of sample problems. Thi…
Machine Learning in Aerodynamic Shape Optimization
Jichao Li, Xiaosong Du, Joaquim R. R. A. Martins
Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep lear…
Learning High-Dimensional Parametric Maps via Reduced Basis Adaptive Residual Networks
Thomas O'Leary-Roseberry, Xiaosong Du, Anirban Chaudhuri +3
We propose a scalable framework for the learning of high-dimensional parametric maps via adaptively constructed residual network (ResNet) maps between reduced bases of the inputs a…
Gradient-enhanced kriging for high-dimensional problems
Mohamed Amine Bouhlel, Joaquim R. R. A. Martins
Surrogate models provide a low computational cost alternative to evaluating expensive functions. The construction of accurate surrogate models with large numbers of independent var…