Skip to Main content Skip to Navigation
Theses

Surrogate modeling of stochastic simulators

Abstract : This thesis is a contribution to the surrogate modeling and the sensitivity analysis on stochastic simulators. Stochastic simulators are a particular type of computational models, they inherently contain some sources of randomness and are generally computationally prohibitive. To overcome this limitation, this manuscript proposes a method to build a surrogate model for stochastic simulators based on Karhunen-Loève expansion. This thesis also aims to perform sensitivity analysis on such computational models. This analysis consists on quantifying the influence of the input variables onto the output of the model. In this thesis, the stochastic simulator is represented by a stochastic process, and the sensitivity analysis is then performed on the differential entropy of this process.The proposed methods are applied to a stochastic simulator assessing the population’s exposure to radio frequency waves in a city. Randomness is an intrinsic characteristic of the stochastic city generator. Meaning that, for a set of city parameters (e.g. street width, building height and anisotropy) does not define a unique city. The context of the electromagnetic dosimetry case study is presented, and a surrogate model is built. The sensitivity analysis is then performed using the proposed method.
Complete list of metadatas

Cited literature [111 references]  Display  Hide  Download

https://tel.archives-ouvertes.fr/tel-02990246
Contributor : Abes Star :  Contact
Submitted on : Thursday, November 5, 2020 - 2:40:45 PM
Last modification on : Saturday, November 7, 2020 - 3:06:46 AM

File

83896_AZZI_2020_archivage.pdf
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-02990246, version 1

Collections

Citation

Soumaya Azzi. Surrogate modeling of stochastic simulators. Applications [stat.AP]. Institut Polytechnique de Paris, 2020. English. ⟨NNT : 2020IPPAT009⟩. ⟨tel-02990246⟩

Share

Metrics

Record views

54

Files downloads

22