7 papers · 1 filter
Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction
Georg Trede, Charlotte Ricarda Doll, Elias Weber +1
Predicting the behavior of dynamical systems (DS) beyond the dynamical and parameter regimes observed in training is a pivotal and essentially unresolved problem in scientific ML.…
Position: A Dynamical Systems Perspective is Needed to Advance Time Series Modeling
Daniel Durstewitz, Christoph Jürgen Hemmer, Florian Hess +2
Time series (TS) modeling has come a long way from early statistical, mainly linear, approaches to the current trend in TS foundation models. With a lot of hype and industrial dema…
The Dynamic-Probabilistic Consistency Gap in Chaotic Surrogate Modeling
Andre Herz, Matthijs Pals, Daniel Durstewitz +1
Dynamical systems reconstruction (DSR) aims to learn surrogate models that capture the dynamics underlying time-series data. Reliably deploying these surrogates requires uncertaint…
Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction
Florian Hess, Florian Götz, Daniel Durstewitz
Reconstructing nonlinear dynamical systems (DS) from data (DSR) is a fundamental challenge in science and engineering, but it inherently relies on sequential models. Recent breakth…
Continuous-Time Piecewise-Linear Recurrent Neural Networks
Alena Brändle, Alena Brändle, Lukas Eisenmann +3
In dynamical systems reconstruction (DSR) we aim to recover the dynamical system (DS) underlying observed time series. Specifically, we aim to learn a generative surrogate model wh…
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data
Manuel Brenner, Elias Weber, Georgia Koppe +1
In science, we are often interested in obtaining a generative model of the underlying system dynamics from observed time series. While powerful methods for dynamical systems recons…