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pro vyhledávání: '"Rogers, Timothy J."'
Classification models are a key component of structural digital twin technologies used for supporting asset management decision-making. An important consideration when developing classification models is the dimensionality of the input, or feature sp
Externí odkaz:
http://arxiv.org/abs/2409.11236
Autor:
Clarkson, Daniel R., Bull, Lawrence A., Wickramarachchi, Chandula T., Cross, Elizabeth J., Rogers, Timothy J., Worden, Keith, Dervilis, Nikolaos, Hughes, Aidan J.
Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitoring), feature-label pairs used
Externí odkaz:
http://arxiv.org/abs/2409.04328
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:
Clarkson, Daniel R., Bull, Lawrence A., Dardeno, Tina A., Wickramarachchi, Chandula T., Cross, Elizabeth J., Rogers, Timothy J., Worden, Keith, Dervilis, Nikolaos, Hughes, Aidan J.
At present, most surface-quality prediction methods can only perform single-task prediction which results in under-utilised datasets, repetitive work and increased experimental costs. To counter this, the authors propose a Bayesian hierarchical model
Externí odkaz:
http://arxiv.org/abs/2310.05807
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
Autor:
Cross, Elizabeth J, Rogers, Timothy J, Pitchforth, Daniel J, Gibson, Samuel J, Jones, Matthew R
Despite the growing availability of sensing and data in general, we remain unable to fully characterise many in-service engineering systems and structures from a purely data-driven approach. The vast data and resources available to capture human acti
Externí odkaz:
http://arxiv.org/abs/2309.10656