Data-driven approximation and reduction from noisy data in matrix pencils 
frameworks

Kergus, Pauline; Gosea, Ion Victor

doi:10.1016/j.ifacol.2022.11.081

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Data-driven approximation and reduction from noisy data in matrix pencils frameworks

Kergus, P., & Gosea, I. V. (2022). Data-driven approximation and reduction from noisy data in matrix pencils frameworks. IFAC-PapersOnLine, 55(30), 371-376.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000C-0462-1 Version Permalink: https://hdl.handle.net/21.11116/0000-000C-0463-0

Genre: Conference Paper

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Copyright 2022 The Authors. This is an Open Access article under the CC-BY-NC-ND license.

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Creators:
Kergus, Pauline¹, Author
Gosea, Ion Victor², Author

Affiliations:
1CNRS, ou_persistent22
2Max Planck Fellow Group for Data-Driven System Reduction and Identification, Max Planck Institute for Dynamics of Complex Technical Systems, Max Planck Society, ou_2453691

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Dates: Date issued: 2022

Publication Status: Issued

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Rev. Type: Peer

Identifiers: DOI: 10.1016/j.ifacol.2022.11.081

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Title: 25th International Symposium on Mathematical Theory of Networks and Systems MTNS 2022

Place of Event: Bayreuth, Germany

Start-/End Date: 2022-09-12 - 2022-09-16

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Title: IFAC-PapersOnLine

Source Genre: Journal

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Pages: - Volume / Issue: 55 (30) Sequence Number: - Start / End Page: 371 - 376 Identifier: ISSN: 24058963