2 citations · 2 across the 6 of their papers we have counts for
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Steerable Neural ODEs on Homogeneous Spaces
Emma Andersdotter, Daniel Persson, Fredrik Ohlsson
We introduce steerable neural ordinary differential equations on homogeneous spaces . These models constitute a novel geometric extension of manifold neural ordinary differe…
Framing local structural identifiability and observability in terms of parameter-state symmetries
Johannes G. Borgqvist, Alexander P. Browning, Fredrik Ohlsson +1
We introduce a subclass of Lie symmetries, called parameter-state symmetries, to analyse the local structural identifiability and observability of mechanistic models consisting of…
PolyNODE: Variable-dimension Neural ODEs on M-polyfolds
Per Åhag, Alexander Friedrich, Fredrik Ohlsson +1
Neural ordinary differential equations (NODEs) are geometric deep learning models based on dynamical systems and flows generated by vector fields on manifolds. Despite numerous suc…