Imperial College London
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DrGiordanoScarciotti

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6268g.scarciotti Website

 
 
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Location

 

1118Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Scarciotti:2022:10.1109/TAC.2021.3050711,
author = {Scarciotti, G and Teel, AR},
doi = {10.1109/TAC.2021.3050711},
journal = {IEEE Transactions on Automatic Control},
pages = {541--556},
title = {On moment matching for stochastic systems},
url = {http://dx.doi.org/10.1109/TAC.2021.3050711},
volume = {67},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - In this paper we study the problem of model reduction by moment matching for stochastic systems. We characterize the mathematical object which generalizes the notion of moment to stochastic differential equations and we find a class of models which achieve moment matching. However, differently from the deterministic case, these reduced order models cannot be considered “simpler” because of the high computational cost paid to determine the moment. To overcome this difficulty, we relax the moment matching problem in two different ways and we present two classes of reduced order models which, approximately matching the stochastic moment, are computationally tractable.
AU  - Scarciotti,G
AU  - Teel,AR
DO  - 10.1109/TAC.2021.3050711
EP  - 556
PY  - 2022///
SN  - 0018-9286
SP  - 541
TI  - On moment matching for stochastic systems
T2  - IEEE Transactions on Automatic Control
UR  - http://dx.doi.org/10.1109/TAC.2021.3050711
UR  - https://ieeexplore.ieee.org/document/9319237
UR  - http://hdl.handle.net/10044/1/86553
VL  - 67
ER  -
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