Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Arbabi, Hassan"'
Any deterministic autonomous dynamical system may be globally linearized by its' Koopman operator. This object is typically infinite-dimensional and can be approximated by the so-called Dynamic Mode Decomposition (DMD). In DMD, the central idea is to
Externí odkaz:
http://arxiv.org/abs/2312.08278
Any autonomous nonlinear dynamical system can be viewed as a superposition of infinitely many linear processes, through the so-called Koopman mode decomposition. Its data-driven approximation- Dynamic Mode Decomposition (DMD)- has been extensively de
Externí odkaz:
http://arxiv.org/abs/2105.03607
We explore the derivation of distributed parameter system evolution laws (and in particular, partial differential operators and associated partial differential equations, PDEs) from spatiotemporal data. This is, of course, a classical identification
Externí odkaz:
http://arxiv.org/abs/2011.08138
Autor:
Arbabi, Hassan, Kevrekidis, Ioannis
Equations governing physico-chemical processes are usually known at microscopic spatial scales, yet one suspects that there exist equations, e.g. in the form of Partial Differential Equations (PDEs), that can explain the system evolution at much coar
Externí odkaz:
http://arxiv.org/abs/2011.04517
Autor:
Arbabi, Hassan, Bunder, Judith E., Samaey, Giovanni, Roberts, Anthony J., Kevrekidis, Ioannis G.
Publikováno v:
JOM 2020
The data-driven discovery of partial differential equations (PDEs) consistent with spatiotemporal data is experiencing a rebirth in machine learning research. Training deep neural networks to learn such data-driven partial differential operators requ
Externí odkaz:
http://arxiv.org/abs/2008.11276
Search and detection of objects on the ocean surface is a challenging task due to the complexity of the drift dynamics and lack of known optimal solutions for the path of the search agents. This challenge was highlighted by the unsuccessful search fo
Externí odkaz:
http://arxiv.org/abs/2004.14110
Autor:
Arbabi, Hassan, Sapsis, Themistoklis
Strongly nonlinear flows, which commonly arise in geophysical and engineering turbulence, are characterized by persistent and intermittent energy transfer between various spatial and temporal scales. These systems are difficult to model and analyze d
Externí odkaz:
http://arxiv.org/abs/1908.08941
Autor:
Arbabi, Hassan, Mezic, Igor
We use spectral analysis of Eulerian and Lagrangian dynamics to study the advective mixing in an incompressible 2D bounded cavity flow. A significant property of such a rotational flow at high Reynolds numbers is that mixing in its core is slower tha
Externí odkaz:
http://arxiv.org/abs/1903.10044
Autor:
Arbabi, Hassan, Mezić, Igor
The classical Prandtl-Batchelor theorem (Prandtl 1904; Batchelor 1956) states that in the regions of steady 2D flow where viscous forces are small and streamlines are closed, the vorticity is constant. In this paper, we extend this theorem to recircu
Externí odkaz:
http://arxiv.org/abs/1808.09398
The Koopman operator theory is an increasingly popular formalism of dynamical systems theory which enables analysis and prediction of the nonlinear dynamics from measurement data. Building on the recent development of the Koopman model predictive con
Externí odkaz:
http://arxiv.org/abs/1804.05291