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FlowSOM: Using self‐organizing maps for visualization and interpretation of cytometry data
A new visualization technique is introduced, called FlowSOM, which analyzes Flow or mass cytometry data using a Self‐Organizing Map, using a two‐level clustering and star charts, to obtain a clear overview of how all markers are behaving on all cells, and to detect subsets that might be missed otherwise. Expand
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
This paper presents a mature, flexible, and adaptive machine learning toolkit for regression modeling and active learning to tackle issues of computational cost and model accuracy. Expand
Fast calculation of multiobjective probability of improvement and expected improvement criteria for Pareto optimization
The authors propose the efficient multiobjective optimization (EMO) algorithm which uses Kriging models and multi objective versions of the probability of improvement and expected improvement criteria to identify the Pareto front with a minimal number of expensive simulations. Expand
Macromodeling of Multiport Systems Using a Fast Implementation of the Vector Fitting Method
Broadband macromodeling of large multiport systems by vector fitting can be time consuming and resource demanding when all elements of the system matrix share a common set of poles. This letterExpand
Orthonormal Vector Fitting: A Robust Macromodeling Tool for Rational Approximation of Frequency Domain Responses
Vector Fitting is widely accepted as a robust macromodeling tool for approximating frequency domain responses of complex physical structures. In this paper, the Orthonormal Vector Fitting techniqueExpand
A Novel Hybrid Sequential Design Strategy for Global Surrogate Modeling of Computer Experiments
A novel hybrid sequential design strategy is proposed which uses a Monte Carlo-based approximation of a Voronoi tessellation for exploration and local linear approximations of the simulator for exploitation, and can be used in heterogeneous modeling environments, where multiple model types are used at the same time. Expand
Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling
An extensive study of new and state-of-the-art space-filling sequential design methods is performed, and it is shown that the new sequential methods proposed are comparable to the best one-shot experimental designs available right now. Expand
GPflowOpt: A Bayesian Optimization Library using TensorFlow
A novel Python framework for Bayesian optimization known as GPflowOpt is introduced. The package is based on the popular GPflow library for Gaussian processes, leveraging the benefits of TensorFlowExpand
A novel sequential design strategy for global surrogate modeling
A comparison is made between different sequential design methods for global surrogate modeling on a real-world electronics problem using a novel hybrid technique which incorporates both an exploitation criterion, using local linear approximations of the objective function, and an exploration criteria, using a Monte Carlo Voronoi tessellation. Expand
ooDACE toolbox: a flexible object-oriented Kriging implementation
An efficient object-oriented Kriging implementation and several Kriged extensions are presented, providing a flexible and easily extendable framework to test and implement new K Riging flavors while reusing as much code as possible. Expand