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pro vyhledávání: '"Champneys, A."'
In the asymptotic limit of a large diffusivity ratio, certain two-component reaction-diffusion (RD) systems can admit localized spike solutions on a 1-D finite domain in a far-from-equilibrium nonlinear regime. It is known that two distinct bifurcati
Externí odkaz:
http://arxiv.org/abs/2411.00763
Autor:
Krause, Andrew L., Klika, Václav, Villar-Sepúveda, Edgardo, Champneys, Alan R., Gaffney, Eamonn A.
Theories of localised pattern formation are important to understand a broad range of natural patterns, but are less well-understood than more established mechanisms of domain-filling pattern formation. Here, we extend recent work on pattern localisat
Externí odkaz:
http://arxiv.org/abs/2409.13043
General conditions are established under which reaction-cross-diffusion systems can undergo spatiotemporal pattern-forming instabilities. Recent work has focused on designing systems theoretically and experimentally to exhibit patterns with specific
Externí odkaz:
http://arxiv.org/abs/2409.06860
Weakly nonlinear amplitude equations are derived for the onset of spatially extended patterns on a general class of $n$-component bulk-surface reaction-diffusion systems in a ball, under the assumption of linear kinetics in the bulk. Linear analysis
Externí odkaz:
http://arxiv.org/abs/2409.06826
In the field of operational modal analysis (OMA), obtained modal information is frequently used to assess the current state of aerospace, mechanical, offshore and civil structures. However, the stochasticity of operational systems and the lack of for
Externí odkaz:
http://arxiv.org/abs/2408.08664
BINDy -- Bayesian identification of nonlinear dynamics with reversible-jump Markov-chain Monte-Carlo
Autor:
Champneys, Max D., Rogers, Timothy J.
Model parsimony is an important \emph{cognitive bias} in data-driven modelling that aids interpretability and helps to prevent over-fitting. Sparse identification of nonlinear dynamics (SINDy) methods are able to learn sparse representations of compl
Externí odkaz:
http://arxiv.org/abs/2408.08062
Autor:
Wilson, James, Champneys, Max D., Tipuric, Matt, Mills, Robin, Wagg, David J., Rogers, Timothy J.
The use of measured vibration data from structures has a long history of enabling the development of methods for inference and monitoring. In particular, applications based on system identification and structural health monitoring have risen to promi
Externí odkaz:
http://arxiv.org/abs/2406.04943
Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting improvements or extensions to existing methods. It is natural, therefore
Externí odkaz:
http://arxiv.org/abs/2405.10779
In engineering, accurately modeling nonlinear dynamic systems from data contaminated by noise is both essential and complex. Established Sequential Monte Carlo (SMC) methods, used for the Bayesian identification of these systems, facilitate the quant
Externí odkaz:
http://arxiv.org/abs/2404.12923
Autor:
Haywood-Alexander, Marcus, Mills, Robin S., Champneys, Max D., Jones, Matthew R., Bonney, Matthew S., Wagg, David, Rogers, Timothy J.
Research developments for structural dynamics in the fields of design, system identification and structural health monitoring (SHM) have dramatically expanded the bounds of what can be learned from measured vibration data. However, significant challe
Externí odkaz:
http://arxiv.org/abs/2310.04478