140 citations · 1.9k across the 86 of their papers we have counts for
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Warped geometric information on the optimisation of Euclidean functions
Marcelo Hartmann, Bernardo Williams, Hanlin Yu +3
We consider the fundamental task of optimising a real-valued function defined in a potentially high-dimensional Euclidean space, such as the loss function in many machine-learning…
Meta-models for transfer learning in source localisation
Lawrence A. Bull, Matthew R. Jones, Elizabeth J. Cross +2
In practice, non-destructive testing (NDT) procedures tend to consider experiments (and their respective models) as distinct, conducted in isolation and associated with independent…
Sobolev Spaces, Kernels and Discrepancies over Hyperspheres
Simon Hubbert, Emilio Porcu, Chris. J. Oates +1
This work provides theoretical foundations for kernel methods in the hyperspherical context. Specifically, we characterise the native spaces (reproducing kernel Hilbert spaces) and…
-DVAE: Physics-Informed Dynamical Variational Autoencoders for Unstructured Data Assimilation
Alex Glyn-Davies, Connor Duffin, Ö. Deniz Akyildiz +1
Incorporating unstructured data into physical models is a challenging problem that is emerging in data assimilation. Traditional approaches focus on well-defined observation operat…
Targeted Separation and Convergence with Kernel Discrepancies
Alessandro Barp, Carl-Johann Simon-Gabriel, Mark Girolami +1
Maximum mean discrepancies (MMDs) like the kernel Stein discrepancy (KSD) have grown central to a wide range of applications, including hypothesis testing, sampler selection, distr…
Fully probabilistic deep models for forward and inverse problems in parametric PDEs
Arnaud Vadeboncoeur, Ömer Deniz Akyildiz, Ieva Kazlauskaite +2
We introduce a physics-driven deep latent variable model (PDDLVM) to learn simultaneously parameter-to-solution (forward) and solution-to-parameter (inverse) maps of parametric par…