Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Sequential modeling of a low noise amplifier with neural networks and active learning
Neural Computing and Applications
Multiobjective global surrogate modeling, dealing with the 5-percent problem
Engineering with Computers
Evolutionary Model Type Selection for Global Surrogate Modeling
The Journal of Machine Learning Research
Evolutionary regression modeling with active learning: an application to rainfall runoff modeling
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
A Novel Hybrid Sequential Design Strategy for Global Surrogate Modeling of Computer Experiments
SIAM Journal on Scientific Computing
ASK: adaptive sampling kit for performance characterization
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
Surrogate modeling of microwave structures using kriging, co-kriging, and space mapping
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields
Structural and Multidisciplinary Optimization
An alternative approach to avoid overfitting for surrogate models
Proceedings of the Winter Simulation Conference
Automatic surrogate model type selection during the optimization of expensive black-box problems
Proceedings of the Winter Simulation Conference
An optimization algorithm employing multiple metamodels and optimizers
International Journal of Automation and Computing
Pareto Optimality in Organelle Energy Metabolism Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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An exceedingly large number of scientific and engineering fields are confronted with the need for computer simulations to study complex, real world phenomena or solve challenging design problems. However, due to the computational cost of these high fidelity simulations, the use of neural networks, kernel methods, and other surrogate modeling techniques have become indispensable. Surrogate models are compact and cheap to evaluate, and have proven very useful for tasks such as optimization, design space exploration, prototyping, and sensitivity analysis. Consequently, in many fields there is great interest in tools and techniques that facilitate the construction of such regression models, while minimizing the computational cost and maximizing model accuracy. This paper presents a mature, flexible, and adaptive machine learning toolkit for regression modeling and active learning to tackle these issues. The toolkit brings together algorithms for data fitting, model selection, sample selection (active learning), hyperparameter optimization, and distributed computing in order to empower a domain expert to efficiently generate an accurate model for the problem or data at hand.