Pregled bibliografske jedinice broj: 1267428
Prediction capabilities of data-driven operator based algorithms on some classes of differential equations
Prediction capabilities of data-driven operator based algorithms on some classes of differential equations // International Conference on Mathematical Analysis and Applications in Science and Engineering – Book of Extended Abstracts / Pinto, Carla M. A. ; Mendonça, Jorge ; Babo, Lurdes ; Baleanu, Dumitru - Porto : ISEP | P.PORTO, 2022 / Porto, Portugal (ur.).
Porto, Portugal: ISEP, P.Porto, 2022. str. 461-464 doi:10.34630/20734 (predavanje, međunarodna recenzija, prošireni sažetak, znanstveni)
CROSBI ID: 1267428 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Prediction capabilities of data-driven operator based algorithms on some classes of differential equations
Autori
Črnjarić-Žic, Nelida ; Maćešić, Senka
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, prošireni sažetak, znanstveni
Izvornik
International Conference on Mathematical Analysis and Applications in Science and Engineering – Book of Extended Abstracts / Pinto, Carla M. A. ; Mendonça, Jorge ; Babo, Lurdes ; Baleanu, Dumitru - Porto : ISEP | P.PORTO, 2022
/ Porto, Portugal - : ISEP, P.Porto, 2022, 461-464
ISBN
978-989-53496-3-0
Skup
The International Conference on Mathematical Analysis and Applications in Science and Engineering (ICMA2SC'22)
Mjesto i datum
Porto, Portugal, 27.06.2022. - 29.06.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
differential equations ; Koopman operator, data-driven algorithms, DMD algorithm, prediction
Sažetak
Nowadays, the possibilities of predicting the behavior of different dynamical systems are in the focus of the research in many scientific disciplines. In this paper we consider the dynamical systems generated by chosen nonlinear and non-autonomous ordinary differential equations. These dynamical systems are approximated by using the Koopman operator based data-driven algorithms applied on the observable functions evaluated at some finite set of the system states. The learned model is then used as a prediction tool to envisage the values of the observable functions in the future system states. The aim of this work is to test and analyze the prediction capabilities of such data-driven algorithms on simple examples in order to make a foundation for understanding their behavior in the prediction task in more complex systems, even in those for which the associated differential equations modeling the system are not known.
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Temeljne tehničke znanosti, Interdisciplinarne tehničke znanosti
POVEZANOST RADA
Projekti:
NadSve-Sveučilište u Rijeci-uniri-prirod-18-118 - Analiza matematičkih modela mehanike fluida i tehničkih sustava pomoću podacima vođenih algoritama za Koopmanov operator (Črnjarić-Žic, Nelida, NadSve ) ( CroRIS)
