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dc.contributor.author
Miller, Jared Franklin
dc.contributor.author
Tacchi, Matteo
dc.contributor.author
Sznaier, Mario
dc.contributor.author
Jasour, Ashkan
dc.date.accessioned
2024-03-21T12:09:48Z
dc.date.available
2024-03-21T07:14:59Z
dc.date.available
2024-03-21T12:09:48Z
dc.date.issued
2024-01-19
dc.identifier.isbn
979-8-3503-0124-3
en_US
dc.identifier.isbn
979-8-3503-0123-6
en_US
dc.identifier.isbn
979-8-3503-0125-0
en_US
dc.identifier.issn
0743-1546
dc.identifier.other
10.1109/CDC49753.2023.10383958
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/665402
dc.description.abstract
This paper proposes an algorithm to upper-bound maximal quantile statistics of a state function over the course of a Stochastic Differential Equation (SDE) system execution. This chance-peak problem is posed as a nonconvex program aiming to maximize the Value-at-Risk (VaR) of a state function along SDE state distributions. The VaR problem is upper-bounded by an infinite-dimensional Second-Order Cone Program in occupation measures through the use of one-sided Cantelli or Vysochanskii-Petunin inequalities. These upper bounds on the true quantile statistics may be approximated from above by a sequence of Semidefinite Programs in increasing size using the moment-Sum-of-Squares hierarchy when all data is polynomial. Effectiveness of this approach is demonstrated on example stochastic polynomial dynamical systems.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.title
Peak Value-at-Risk Estimation for Stochastic Differential Equations using Occupation Measures
en_US
dc.type
Conference Paper
ethz.book.title
2023 62nd IEEE Conference on Decision and Control (CDC)
en_US
ethz.pages.start
4836
en_US
ethz.pages.end
4842
en_US
ethz.event
62nd IEEE Conference on Decision and Control (CDC 2023)
en_US
ethz.event.location
Singapore
ethz.event.date
December 13-15, 2023
en_US
ethz.notes
Conference lecture held on December 14, 2023.
en_US
ethz.grant
Modeling, Identification and Control of Periodic Systems in Energy Applications
en_US
ethz.grant
NCCR Automation (phase I)
en_US
ethz.identifier.wos
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02650 - Institut für Automatik / Automatic Control Laboratory
en_US
ethz.grant.agreementno
178890
ethz.grant.agreementno
180545
ethz.grant.fundername
SNF
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
Projekte MINT
ethz.grant.program
NCCR full proposal
ethz.date.deposited
2024-03-21T07:15:05Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2024-03-21T12:09:49Z
ethz.rosetta.lastUpdated
2024-03-21T12:09:49Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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