Groundwater flow meta-model for multilevel Monte Carlo methods

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F21%3A00008996" target="_blank" >RIV/46747885:24220/21:00008996 - isvavai.cz</a>
Result on the web
<a href="http://ceur-ws.org/Vol-2962/paper40.pdf" target="_blank" >http://ceur-ws.org/Vol-2962/paper40.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Groundwater flow meta-model for multilevel Monte Carlo methods
Original language description
The paper presents a meta-modeling-based technique to reduce the computational cost of Monte Carlo methods. A stochastic simulation of groundwater flow is substituted with a graph neural network meta-model. This type of neural network can deal with a non-euclidean structure of the input data, which in our case is a graph representing a random field on an unstructured mesh. In order to find the most suitable meta-model, a comparison of the standard support vector regression with spectral and spatial graph convolutional neural networks is provided. Both the Monte Carlo method and the multilevel Monte Carlo method are extended by the meta-model level. In both cases, up to 50% savings in computational cost are achieved while maintaining the accuracy of Monte Carlo estimates.
Czech name
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Czech description
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Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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