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
Pareto-Based Multi-output Model Type Selection
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
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
Generating sequential space-filling designs using genetic algorithms and Monte Carlo methods
SEAL'10 Proceedings of the 8th international conference on Simulated evolution and learning
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
Comparison of modeling techniques in circuit variability analysis
International Journal of Numerical Modelling: Electronic Networks, Devices and Fields
An alternative approach to avoid overfitting for surrogate models
Proceedings of the Winter Simulation Conference
Enhanced metamodeling techniques for high-dimensional IC design estimation problems
Proceedings of the Conference on Design, Automation and Test in Europe
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The use of global surrogate models has become commonplace as a cost effective alternative for performing complex high fidelity computer simulations. Due to their compact formulation and negligible evaluation time, global surrogate models are very useful tools for exploring the design space, what-if analysis, optimization, prototyping, visualization, and sensitivity analysis. Neural networks have been proven particularly useful in this respect due to their ability to model high dimensional, non-linear responses accurately. In this article, we present the results of an extensive study on the performance of neural networks as compared to other modeling techniques in the context of active learning. We investigate the scalability and accuracy in function of the number design variables and number of datapoints. The case study under consideration is a high dimensional, parametrized low noise amplifier RF circuit block.