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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…
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…
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…