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stat.ML2026
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…
stat.ML2025
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…