Imperial College London
Portrait Picture

Prof Francesco Montomoli

Faculty of Engineering, Department of Aeronautics

Professor in Computational Aerodynamics
 
 
 
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Contact

 

+44 (0)20 7594 5151f.montomoli Website

 
 
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Location

 

215City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Pepper:2021:10.1016/j.cma.2020.113577,
author = {Pepper, N and Montomoli, F and sharma, S},
doi = {10.1016/j.cma.2020.113577},
journal = {Computer Methods in Applied Mechanics and Engineering},
title = {Data fusion for Uncertainty Quantification with Non-intrusive Polynomial Chaos},
url = {http://dx.doi.org/10.1016/j.cma.2020.113577},
volume = {374},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This work presents a framework for updating an estimate of a probability distribution, arising from an uncertainty propagation using Non-intrusive Polynomial Chaos (NIPC), with scarce experimental measurements of a Quantity of Interest (QoI). In recent years much work has been directed towards developing methods of combining models of different accuracies in order to propagate uncertainty, but the problem of improving uncertainty propagations by considering evidence from both computational models and experiments has received less attention.The framework described here uses the Maximum Entropy Principle (MEP) to find an updated, least biased estimate of a probability distribution by maximising the entropy between the original and updated estimates. A constrained optimisation is performed to find the coefficients of a Polynomial Chaos Expansion (PCE) that minimise the Kullback–Leibler (KL) divergence between estimates, while ensuring that the new estimate conforms to constraints imposed by the available experimental measurements of the QoI. In this work a novel constraint is used, based upon the Dvoretzky–Kiefer–Wolfowitz inequality and the Massart bound (DKWM), as opposed to the more commonly used moment-based constraints. Such a constraint allows scarce experimental data to be used in informing the updated estimate of the probability distribution.
AU  - Pepper,N
AU  - Montomoli,F
AU  - sharma,S
DO  - 10.1016/j.cma.2020.113577
PY  - 2021///
SN  - 0045-7825
TI  - Data fusion for Uncertainty Quantification with Non-intrusive Polynomial Chaos
T2  - Computer Methods in Applied Mechanics and Engineering
UR  - http://dx.doi.org/10.1016/j.cma.2020.113577
UR  - http://hdl.handle.net/10044/1/85115
VL  - 374
ER  -
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