Abstract
It is difficult to provide live simulation systems for decision support. Time is limited and uncertainty quantification requires many simulation runs. We combine a surrogate model with the stochastic collocation method to overcome time and storage restrictions and show a proof of concept for a de-boarding scenario of a train.
Highlights
When trying to assess whether or not the passengers of a train can safely de-board onto an already over-full platform a safety officer faces a number of uncertainties
The Closed Observable Surrogate Model (COSM) To overcome the speed and storage barriers, we propose a low dimensional surrogate model that captures the dynamic of the original data and that allows observation of the quantity of interest
Fast and cheap surrogate models to reproduce simulation data or measured data are constructed using time delayed embedding for uniqueness and diffusion maps for subsequent order reduction
Summary
1. Introduction When trying to assess whether or not the passengers of a train can safely de-board onto an already over-full platform a safety officer faces a number of uncertainties. Chief among them are the initial numbers of passengers on the train and on the platform. One obtains probability distributions of quantities of interest, in our case, the number of passengers on the platform.
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