Design of experiments for fitting subsystem metamodels
Proceedings of the 29th conference on Winter simulation
Local function approximation in evolutionary algorithms for the optimization of costly functions
IEEE Transactions on Evolutionary Computation
Max-min surrogate-assisted evolutionary algorithm for robust design
IEEE Transactions on Evolutionary Computation
Grid-Enabled Adaptive Metamodeling and Active Learning for Computer Based Design
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Evolutionary Model Type Selection for Global Surrogate Modeling
The Journal of Machine Learning Research
A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
The Journal of Machine Learning Research
A novel sequential design strategy for global surrogate modeling
Winter Simulation Conference
A Novel Hybrid Sequential Design Strategy for Global Surrogate Modeling of Computer Experiments
SIAM Journal on Scientific Computing
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Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alternative. However, due to the computational cost of these high fidelity simulations, the use of neural networks, kernel methods, and other surrogate modeling techniques has become indispensable. Surrogate models are compact and cheap to evaluate, and have proven very useful for tasks such as optimization, design space exploration, visualization, prototyping, and sensitivity analysis. Consequently, there is great interest in techniques that facilitate the construction of such regression models, while minimizing the computational cost and maximizing model accuracy. The model calibration problem in rainfall runoff modeling is an important problem from hydrology that can benefit from advances in surrogate modeling and machine learning in general. This paper presents a novel, fully automated approach to tackling this problem. Drawing upon advances in machine learning, hyperparameter optimization, model type selection, and sample selection (active learning) are all handled automatically. Increasing the utility of such methods for the domain expert.