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Pregled bibliografske jedinice broj: 1206590

Learning Low-Dimensional Dynamical-System Models from Noisy Frequency-Response Data with Loewner Rational Interpolation

Drmač, Zlatko; Peherstorfer, Benjamin
Learning Low-Dimensional Dynamical-System Models from Noisy Frequency-Response Data with Loewner Rational Interpolation // Realization and Model Reduction of Dynamical Systems / Beattie, Christopher ; Benner, Peter ; Embree, Mark ; Gugercin, Serkan ; Lefteriu, Sanda (ur.)., 2022. str. 39-57 doi:10.1007/978-3-030-95157-3_3

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Naslov
Learning Low-Dimensional Dynamical-System Models from Noisy Frequency-Response Data with Loewner Rational Interpolation

Autori
Drmač, Zlatko ; Peherstorfer, Benjamin

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, znanstveni

Knjiga
Realization and Model Reduction of Dynamical Systems

Urednik/ci
Beattie, Christopher ; Benner, Peter ; Embree, Mark ; Gugercin, Serkan ; Lefteriu, Sanda

Izdavač
Springer

Godina
2022

Raspon stranica
39-57

ISBN
978-3-030-95156-6

Ključne riječi
model reduction, dynamical systems, concentration inequalities, system identification

Sažetak
Loewner rational interpolation provides a versatile tool to learn low-dimensional dynamical-system models from frequency-response measurements. This work investigates the robustness of the Loewner approach to noise. The key finding is that if the measurements are polluted with Gaussian noise, then the error due to noise grows at most linearly with the standard deviation with high probability under certain conditions. The analysis gives insights into making the Loewner approach robust against noise via linear transformations and judicious selections of measurements. Numerical results demonstrate the linear growth of the error on benchmark examples.

Izvorni jezik
Engleski

Znanstvena područja
Matematika



POVEZANOST RADA

Projekti:
HRZZ-IP-2019-04-6268 - Stohastičke aproksimacije malog ranga i primjene na parametarski ovisne probleme (RandLRAP) (Grubišić, Luka, HRZZ - 2019-04) ( CroRIS)

Ustanove:
Prirodoslovno-matematički fakultet, Matematički odjel, Zagreb,
Prirodoslovno-matematički fakultet, Zagreb

Profili:

Avatar Url Zlatko Drmač (autor)

Poveznice na cjeloviti tekst rada:

doi link.springer.com

Citiraj ovu publikaciju:

Drmač, Zlatko; Peherstorfer, Benjamin
Learning Low-Dimensional Dynamical-System Models from Noisy Frequency-Response Data with Loewner Rational Interpolation // Realization and Model Reduction of Dynamical Systems / Beattie, Christopher ; Benner, Peter ; Embree, Mark ; Gugercin, Serkan ; Lefteriu, Sanda (ur.)., 2022. str. 39-57 doi:10.1007/978-3-030-95157-3_3
Drmač, Z. & Peherstorfer, B. (2022) Learning Low-Dimensional Dynamical-System Models from Noisy Frequency-Response Data with Loewner Rational Interpolation. U: Beattie, C., Benner, P., Embree, M., Gugercin, S. & Lefteriu, S. (ur.) Realization and Model Reduction of Dynamical Systems., Springer, str. 39-57 doi:10.1007/978-3-030-95157-3_3.
@inbook{inbook, author = {Drma\v{c}, Zlatko and Peherstorfer, Benjamin}, year = {2022}, pages = {39-57}, DOI = {10.1007/978-3-030-95157-3\_3}, keywords = {model reduction, dynamical systems, concentration inequalities, system identification}, doi = {10.1007/978-3-030-95157-3\_3}, isbn = {978-3-030-95156-6}, title = {Learning Low-Dimensional Dynamical-System Models from Noisy Frequency-Response Data with Loewner Rational Interpolation}, keyword = {model reduction, dynamical systems, concentration inequalities, system identification}, publisher = {Springer} }
@inbook{inbook, author = {Drma\v{c}, Zlatko and Peherstorfer, Benjamin}, year = {2022}, pages = {39-57}, DOI = {10.1007/978-3-030-95157-3\_3}, keywords = {model reduction, dynamical systems, concentration inequalities, system identification}, doi = {10.1007/978-3-030-95157-3\_3}, isbn = {978-3-030-95156-6}, title = {Learning Low-Dimensional Dynamical-System Models from Noisy Frequency-Response Data with Loewner Rational Interpolation}, keyword = {model reduction, dynamical systems, concentration inequalities, system identification}, publisher = {Springer} }

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