Transport map Bayesian parameter estimation for dynamical systems

Abstract: Accurate online state and parameter estimation of uncertain non‐linear dynamical systems is a demanding task that has been traditionally handled by adopting non‐linear Kalman Filters or particle filters. However, in case of Kalman filters the system needs to be linearised and for particle filters the computational demand can be high. Recent advances in optimal transport theory and the application to Bayesian model updating pave the way for other approaches to system and parameter identification. They also provide a way of formulating the problem in such a way that efficient online estimation for complex systems is possible. In this work, we investigate the properties of the transport map approach when compared to standard Markov Chain Monte Carlo in an off‐line setting as a first step towards on‐line parameter estimation. We apply both approaches to an analytical exponential model and a dynamical system with seven unknown parameters subjected to ground displacement. Details on the theory of transport maps and on the used MCMC algorithm are also given.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Transport map Bayesian parameter estimation for dynamical systems ; volume:23 ; number:1 ; year:2023 ; extent:6 ; Proceedings in applied mathematics and mechanics ; 23, Heft 1 (2023) (gesamt 6)

Beteiligte, Orts- und Zeitangaben

• Urheber: Grashorn, Jan ; Urrea-Quintero, Jorge-Humberto ; Broggi, Matteo ; Chamoin, Ludovic ; Beer, Michael

Weitere Informationen

• DOI: 10.1002/pamm.202200136
• URN: urn:nbn:de:101:1-2023060115163328119337
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 14.08.2025, 08:45 MESZ