15 citations · 43 across the 12 of their papers we have counts for
3 papers · 1 filter
Discovering Efficient Periodic Behaviours in Mechanical Systems via Neural Approximators
Yannik Wotte, Sven Dummer, Nicolò Botteghi +3
It is well known that conservative mechanical systems exhibit local oscillatory behaviours due to their elastic and gravitational potentials, which completely characterise these pe…
Duality for Neural Networks through Reproducing Kernel Banach Spaces
Len Spek, Tjeerd Jan Heeringa, Felix Schwenninger +1
Reproducing Kernel Hilbert spaces (RKHS) have been a very successful tool in various areas of machine learning. Recently, Barron spaces have been used to prove bounds on the genera…
Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data
Nicolò Botteghi, Mengwu Guo, Christoph Brune
This work proposes a Stochastic Variational Deep Kernel Learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The fram…