2 citations · 2 across the 6 of their papers we have counts for
6 papers
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…
Framing structural identifiability in terms of parameter symmetries
Johannes G Borgqvist, Alexander P Browning, Fredrik Ohlsson +1
A key step in mechanistic modelling of dynamical systems is to conduct a structural identifiability analysis. This entails deducing which parameter combinations can be estimated fr…
Equivariant Manifold Neural ODEs and Differential Invariants
Emma Andersdotter, Daniel Persson, Fredrik Ohlsson
In this paper, we develop a manifestly geometric framework for equivariant manifold neural ordinary differential equations (NODEs) and use it to analyse their modelling capabilitie…
Construction of integrable generalised travelling wave models and analytical solutions using Lie symmetries
Johannes G. Borgqvist, Fredrik Ohlsson, Xingjian Zhou +1
Certain solutions of autonomous PDEs without any boundary conditions describing the spatiotemporal evolution of a dependent variable in an unbounded spatial domain can be character…