Toward interpretable #AI foundation models for #DynamicalSystems reconstruction: Our paper on transfer & few-shot learning for dynamical systems just got accepted for #ICLR2025 ! Previous version: arxiv.org/pdf/2410.04814; strongly updated version will be available soon ... (1/4)

We show applications like transfer & few-shot learning, but most interestingly perhaps, subject/system-specific features were often linearly related to control parameters of the underlying dynamical system trained on … (2/4)

This gives rise to an interpretable latent feature space, where datasets with similar dynamics cluster. Intriguingly, this clustering according to *dynamical systems features* led to much better separation of groups than could be achieved by more trad. time series features. (3/4)