WeLehmberg, DanielDietrich, FelixKevrekidis, Ioannis G.Bungartz, Hans-JoachimKöster, Gerta apply the Koopman operatorKoopman operator framework to pedestrian dynamicsPedestrian dynamics. In an example scenario, we generate crowd density time series data with a microscopic pedestrian simulator. We then approximate the Koopman operatorKoopman operator in matrix form through Extended Dynamic Mode Decomposition, using Geometric Harmonics on the data as a dictionary. The Koopman matrix is integrated into a surrogate modelSurrogate model, which allows to approximate crowd density time series data to be generated, independently from the original microscopic simulator. The evaluation of the constructed surrogate modelSurrogate model is orders of magnitude faster, and enables us to use methods that require many model evaluations.
Exploring Koopman Operator Based Surrogate Models—Accelerating the Analysis of Critical Pedestrian Densities
Springer Proceedings Phys.
2020-11-17
9 pages
Article/Chapter (Book)
Electronic Resource
English
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