5 citations · 7 across the 5 of their papers we have counts for
5 papers
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…
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…
Optimal Recurrent Network Topologies for Dynamical Systems Reconstruction
Christoph Jürgen Hemmer, Manuel Brenner, Florian Hess +1
In dynamical systems reconstruction (DSR) we seek to infer from time series measurements a generative model of the underlying dynamical process. This is a prime objective in any sc…
Out-of-Domain Generalization in Dynamical Systems Reconstruction
Niclas Göring, Florian Hess, Manuel Brenner +2
In science we are interested in finding the governing equations, the dynamical rules, underlying empirical phenomena. While traditionally scientific models are derived through cycl…
Integrating Multimodal Data for Joint Generative Modeling of Complex Dynamics
Manuel Brenner, Florian Hess, Georgia Koppe +1
Many, if not most, systems of interest in science are naturally described as nonlinear dynamical systems. Empirically, we commonly access these systems through time series measurem…