Zobrazeno 1 - 10
of 225
pro vyhledávání: '"Niven, Robert K."'
Publikováno v:
Chaos 34, 083140 (2024)
This study presents a Bayesian maximum \textit{a~posteriori} (MAP) framework for dynamical system identification from time-series data. This is shown to be equivalent to a generalized Tikhonov regularization, providing a rational justification for th
Externí odkaz:
http://arxiv.org/abs/2401.16943
Autor:
Niven, Robert K.
We propose an additional category of dimensionless groups based on the principle of {\it entropic similarity}, defined by ratios of (i) entropy production terms; (ii) entropy flow rates or fluxes; or (iii) information flow rates or fluxes. Since all
Externí odkaz:
http://arxiv.org/abs/2301.12665
Autor:
Niven, Robert K.
The Reynolds transport theorem occupies a central place in fluid dynamics, providing a generalized integral conservation equation for the transport of any conserved quantity within a fluid, and connected to its corresponding differential equation. Re
Externí odkaz:
http://arxiv.org/abs/2101.06113
Publikováno v:
In Science of the Total Environment 1 February 2023 858 Part 3
The Reynolds transport theorem provides a generalized conservation law for the transport of a conserved quantity by fluid flow through a continuous connected control volume. It is close connected to the Liouville equation for the conservation of a lo
Externí odkaz:
http://arxiv.org/abs/1810.06022
Autor:
Niven, Robert K.1 (AUTHOR) r.niven@adfa.edu.au
Publikováno v:
Entropy. Nov2023, Vol. 25 Issue 11, p1538. 36p.
Autor:
Nahar, Kamrun1 (AUTHOR) kamrun.nahar@unisq.edu.au, Niven, Robert K.2 (AUTHOR) r.niven@adfa.edu.au
Publikováno v:
Agronomy. Oct2023, Vol. 13 Issue 10, p2539. 11p.
Publikováno v:
In Science of the Total Environment 20 May 2022 822
Publikováno v:
Nonlinear Dynamics 91 (2), 1001-1021, 2018
Networks of coupled dynamical systems provide a powerful way to model systems with enormously complex dynamics, such as the human brain. Control of synchronization in such networked systems has far reaching applications in many domains, including eng
Externí odkaz:
http://arxiv.org/abs/1612.05276
We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplifie
Externí odkaz:
http://arxiv.org/abs/1602.04648