61 citations · 75 across the 9 of their papers we have counts for
10 papers
Orthogonal Discrepancy Kernels for Learning with Partial Physics
Swapnil Manna, Timothy J. Rogers, Lawrence Bull
We introduce a semi-parametric framework for nonlinear system identification, which decouples discrepancy functions from physics-based components. Orthogonal Gaussian process regre…
Fundamental Limits of Stability Inference in High-Dimensional Complex Systems
Michela Costa, Kentaro Hoshisashi, Flaviano Morone +2
Many complex systems, including ecosystems, neural circuits, and financial markets, are inferred to operate close to a threshold of instability, at which a small perturbation can p…
Towards real-time surrogate-free Bayesian inversion for neutron reflectometry
Max D. Champneys, Andrew J. Parnell, Philipp Gutfreund +4
Neutron reflectometry (NR) is a key enabling technology for many areas of scientific development. Although the forward reflectivity model is well-known, inferring the physical prop…
A new perspective on Bayesian Operational Modal Analysis
Brandon J. O'Connell, Max D. Champneys, Timothy J. Rogers
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.…
BINDy -- Bayesian identification of nonlinear dynamics with reversible-jump Markov-chain Monte-Carlo
Max D. Champneys, Timothy J. Rogers
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 dyna…
Multiple-input, multiple-output modal testing of a Hawk T1A aircraft: A new full-scale dataset for structural health monitoring
James Wilson, Max D. Champneys, Matt Tipuric +3
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 syst…
Baseline Results for Selected Nonlinear System Identification Benchmarks
Max D. Champneys, Gerben I. Beintema, Roland Tóth +2
Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting im…
Probabilistic Numeric SMC Sampling for Bayesian Nonlinear System Identification in Continuous Time
Joe D. Longbottom, Max D. Champneys, Timothy J. Rogers
In engineering, accurately modeling nonlinear dynamic systems from data contaminated by noise is both essential and complex. Established Sequential Monte Carlo (SMC) methods, used…
Full-scale modal testing of a Hawk T1A aircraft for benchmarking vibration-based methods
Marcus Haywood-Alexander, Robin S. Mills, Max D. Champneys +4
Research developments for structural dynamics in the fields of design, system identification and structural health monitoring (SHM) have dramatically expanded the bounds of what ca…
Physics-informed machine learning for Structural Health Monitoring
Elizabeth J Cross, Samuel J Gibson, Matthew R Jones +3
The use of machine learning in Structural Health Monitoring is becoming more common, as many of the inherent tasks (such as regression and classification) in developing condition-b…