Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Jan Heiland"'
Publikováno v:
Frontiers in Applied Mathematics and Statistics, Vol 8 (2022)
The control of general nonlinear systems is a challenging task in particular for large-scale models as they occur in the semi-discretization of partial differential equations (PDEs) of, say, fluid flow. In order to employ powerful methods from linear
Externí odkaz:
https://doaj.org/article/3e0168b4143a4285a7df5ca3997e6242
Autor:
Jan Heiland, Benjamin Unger
Publikováno v:
Mathematics, Vol 10, Iss 3, p 418 (2022)
Dynamic mode decomposition (DMD) is a popular data-driven framework to extract linear dynamics from complex high-dimensional systems. In this work, we study the system identification properties of DMD. We first show that DMD is invariant under linear
Externí odkaz:
https://doaj.org/article/d432f7bb2f8a41a48d5a8e081c60c7dc
Publikováno v:
AIMS Mathematics, Vol 1, Iss 3, Pp 261-281 (2016)
Over the recent years the importance of numerical experiments has gradually been more recognized. Nonetheless, su cient documentation of how computational results have been obtained is often not available. Especially in the scientific computing and a
Externí odkaz:
https://doaj.org/article/2220a9632d924a2d8d0ab7e48507c41c
Autor:
Peter Benner, Jan Heiland
Publikováno v:
ScienceOpen Research (2015)
For the modelling and the numerical approximation of problems with time-dependent Dirichlet boundary conditions one can call on several consistent and inconsistent approaches. We show that spatially discretized boundary control problems can be brough
Externí odkaz:
https://doaj.org/article/2515a84739004daea42c96caa6d3a265
Autor:
Jan Heiland, Peter Benner
Publikováno v:
International Journal for Numerical Methods in Engineering. 124:2801-2817
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d2dc79202b1e4b005c28ab75f14d1a5b
https://doi.org/10.1002/nme.7229
https://doi.org/10.1002/nme.7229
Publikováno v:
Proceedings of Applied Mathematics and Mechanics
Linear projection schemes like Proper Orthogonal Decomposition can efficiently reduce the dimensions of dynamical systems but are naturally limited, e.g., for convection-dominated problems. Nonlinear approaches have shown to outperform linear methods
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2998293c5fe95673678a25e231e25bd1
https://hdl.handle.net/21.11116/0000-000B-331B-E21.11116/0000-000B-331D-C21.11116/0000-000D-46EF-8
https://hdl.handle.net/21.11116/0000-000B-331B-E21.11116/0000-000B-331D-C21.11116/0000-000D-46EF-8
Publikováno v:
Numerical Algorithms
Algebraic Riccati equations with indefinite quadratic terms play an important role in applications related to robust controller design. While there are many established approaches to solve these in case of small-scale dense coefficients, there is no
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::209cfd02fb4a47f4c49dde0d292ec5e5
https://hdl.handle.net/21.11116/0000-0009-7D49-A21.11116/0000-0009-7D4B-821.11116/0000-000C-D93D-C
https://hdl.handle.net/21.11116/0000-0009-7D49-A21.11116/0000-0009-7D4B-821.11116/0000-000C-D93D-C
Publikováno v:
IFAC-Papers
The optimization of a controlled process in a simulation without access to the model itself is a common scenario and very relevant to many chemical engineering applications. A general approach is to apply a black-box optimization algorithm to a param
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4f927fa52f954cdb47cf5f9b422ca01b
https://doi.org/10.1016/j.ifacol.2021.08.229
https://doi.org/10.1016/j.ifacol.2021.08.229
Autor:
Unger, Jan Heiland, Benjamin
Publikováno v:
Mathematics; Volume 10; Issue 3; Pages: 418
Dynamic mode decomposition (DMD) is a popular data-driven framework to extract linear dynamics from complex high-dimensional systems. In this work, we study the system identification properties of DMD. We first show that DMD is invariant under linear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::703817cb2fe1bf80f600b5c2d7ba3dc9