Research Article
Directional Grid-Based Search for Simulation Metamodeling Using Active Learning
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@INPROCEEDINGS{10.1007/978-3-030-38822-5_3, author={Francisco Antunes and Francisco Pereira and Bernardete Ribeiro}, title={Directional Grid-Based Search for Simulation Metamodeling Using Active Learning}, proceedings={Intelligent Transport Systems. From Research and Development to the Market Uptake. Third EAI International Conference, INTSYS 2019, Braga, Portugal, December 4--6, 2019}, proceedings_a={INTSYS}, year={2020}, month={1}, keywords={Machine learning Active learning Simulation metamodeling Gaussian Processes}, doi={10.1007/978-3-030-38822-5_3} }- Francisco Antunes
Francisco Pereira
Bernardete Ribeiro
Year: 2020
Directional Grid-Based Search for Simulation Metamodeling Using Active Learning
INTSYS
Springer
DOI: 10.1007/978-3-030-38822-5_3
Abstract
Within dense urban environments, real-world transportation systems are often associated with extraordinary modeling complexity. Where standard analytic methods tend to fail, simulation tools emerge as reliable approaches to study such systems. Despite their versatility, simulation models can prove to be computational burdens, exhibiting prohibitive simulation runtimes. To address this shortcoming, metamodels are used to aid in the simulation modeling process.
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