Zobrazeno 1 - 10
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pro vyhledávání: '"A, Heiland"'
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
Autor:
Hoyo, Junis Heiland, Sikora, Bastian
The hyperfine structure of bound electrons in hydrogen-like ions is considered with corrections to the energy levels due to vacuum polarization (VP). Corrections to the wave function as well as the magnetic potential are determined for both leptonic
Externí odkaz:
http://arxiv.org/abs/2410.09161
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
Autor:
Ruscone, Marco, Checcoli, Andrea, Heiland, Randy, Barillot, Emmanuel, Macklin, Paul, Calzone, Laurence, Noël, Vincent
Multiscale models provide a unique tool for studying complex processes that study events occurring at different scales across space and time. In the context of biological systems, such models can simulate mechanisms happening at the intracellular lev
Externí odkaz:
http://arxiv.org/abs/2406.18371
Publikováno v:
Conference on Advanced Enterprise Information System (AEIS 2023)
In this paper, we propose a method for aligning models with their realization through the application of model-based systems engineering. Our approach is divided into three steps. (1) Firstly, we leverage domain expertise and the Unified Architecture
Externí odkaz:
http://arxiv.org/abs/2407.09513
Publikováno v:
Conference on Perspectives in Business Informatics Research (BIR 2023)
Reference models in form of best practices are an essential element to ensured knowledge as design for reuse. Popular modeling approaches do not offer mechanisms to embed reference models in a supporting way, let alone a repository of it. Therefore,
Externí odkaz:
http://arxiv.org/abs/2407.00064
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