Zobrazeno 1 - 10
of 116
pro vyhledávání: '"Heiland Jan"'
We present a data-driven approach to use the Koopman generator for prediction and optimal control of control-affine stochastic systems. We provide a novel conceptual approach and a proof-of-principle for the determination of optimal control policies
Externí odkaz:
http://arxiv.org/abs/2410.09452
We consider the task of data-driven identification of dynamical systems, specifically for systems whose behavior at large frequencies is non-standard, as encoded by a non-trivial relative degree of the transfer function or, alternatively, a non-trivi
Externí odkaz:
http://arxiv.org/abs/2410.02000
Publikováno v:
Analele Stiintifice ale Universitatii Ovidius Constanta: Seria Matematica, Vol 26, Iss 2, Pp 11-40 (2018)
The optimal control of moving boundary problems receives growing attention in science and technology. We consider the so called two-phase Stefan problem that models a solid and a liquid phase separated by a moving interface. The Stefan problem is cou
Externí odkaz:
https://doaj.org/article/b3e2f67e91064ec9b23572dc01de20d0
Polytopic autoencoders provide low-dimensional parametrizations of states in a polytope. For nonlinear PDEs, this is readily applied to low-dimensional linear parameter-varying (LPV) approximations as they have been exploited for efficient nonlinear
Externí odkaz:
http://arxiv.org/abs/2403.18044
Numerical algorithms and computational tools are instrumental in navigating and addressing complex simulation and data processing tasks. The exponential growth of metadata and parameter-driven simulations has led to an increasing demand for automated
Externí odkaz:
http://arxiv.org/abs/2405.00028
Autor:
Heiland, Jan, Kim, Yongho
With the advancement of neural networks, there has been a notable increase, both in terms of quantity and variety, in research publications concerning the application of autoencoders to reduced-order models. We propose a polytopic autoencoder archite
Externí odkaz:
http://arxiv.org/abs/2401.10620
Autor:
Gosea, Ion Victor, Heiland, Jan
The reduced-order modeling of a system from data (also known as system identification) is a classical task in system and control theory and well understood for standard linear systems with the so-called Loewner framework as one of many established ap
Externí odkaz:
http://arxiv.org/abs/2311.05519
Autor:
Das, Amritam, Heiland, Jan
The control of nonlinear large-scale dynamical models such as the incompressible Navier-Stokes equations is a challenging task. The computational challenges in the controller design come from both the possibly large state space and the nonlinear dyna
Externí odkaz:
http://arxiv.org/abs/2311.05305
The generalized Kalman-Yakubovich-Popov lemma as established by Iwasaki and Hara in 2005 marks a milestone in the analysis and synthesis of linear systems from a finite-frequency perspective. Given a pre-specified frequency band, it allows us to prod
Externí odkaz:
http://arxiv.org/abs/2306.01120
Autor:
Heiland, Jan, Werner, Steffen W. R.
Publikováno v:
IEEE Control Syst. Lett., 7:3012-3017, 2023
Nonlinear feedback design via state-dependent Riccati equations is well established but unfeasible for large-scale systems because of computational costs. If the system can be embedded in the class of linear parameter-varying (LPV) systems with the p
Externí odkaz:
http://arxiv.org/abs/2303.11515