4 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 +5
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…
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…