Bayesian approach to parameter identification in gas networks

• Autorzy: Hajian Soheil , Hintermüller Michael , Schillings Claudia , Strogies Nikolai
• Języki publikacji: EN

Abstrakty

EN

The inverse problem of identifying the friction coefficient in an isothermal semilinear Euler system is considered. Adopting a Bayesian approach, the goal is to identify the distribution of the quantity of interest based on a finite number of noisy measurements of the pressure at the boundaries of the domain. First wellposedness of the underlying non-linear PDE system is shown using semigroup theory, and then Lipschitz continuity of the solution operator with respect to the friction coefficient is established. Based on the Lipschitz property, well-posedness of the resulting Bayesian inverse problem for the identification of the friction coefficient is inferred. Numerical tests for scalar and distributed parameters are performed to validate the theoretical results.

Słowa kluczowe

EN

Bayesian inversion; distributed friction coefficient; gas network/pipeline; hyperbolic PDE system

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2019
• Tom: Vol. 48, No. 2
• Strony: 377--402
• Opis fizyczny: Bibliogr. 36 poz., rys., tab.

Twórcy

autor

Hajian Soheil

• Institut für Mathematik, Humboldt–Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany

autor

Hintermüller Michael

• Institut für Mathematik, Humboldt–Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany
• Weierstrass Institute, Mohrenstrasse 39, 10117 Berlin, Germany

autor

Schillings Claudia

• Institute for Mathematics, University of Mannheim, B6 28-29, C 306; 68131 Mannheim, Germany

autor

Strogies Nikolai

• Institut für Mathematik, Humboldt–Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).