1 citations · 1 across the 4 of their papers we have counts for
8 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…