2 citations · 5 across the 7 of their papers we have counts for
8 papers
A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems
Christoph Jürgen Hemmer, Florian Plaswig, Daniel Durstewitz
Recent foundation models (FMs) for zero-shot reconstruction of dynamical systems (DS) achieve strong out-of-domain generalization but provide little insight into the mechanisms tha…
Detecting Invariant Manifolds in ReLU-Based RNNs
Lukas Eisenmann, Alena Brändle, Zahra Monfared +1
Recurrent Neural Networks (RNNs) have found widespread applications in machine learning for time series prediction and dynamical systems reconstruction, and experienced a recent re…
True Zero-Shot Inference of Dynamical Systems Preserving Long-Term Statistics
Christoph Jürgen Hemmer, Daniel Durstewitz
Complex, temporally evolving phenomena, from climate to brain activity, are governed by dynamical systems (DS). DS reconstruction (DSR) seeks to infer generative surrogate models o…
Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction
Manuel Brenner, Christoph Jürgen Hemmer, Zahra Monfared +1
Dynamical systems (DS) theory is fundamental for many areas of science and engineering. It can provide deep insights into the behavior of systems evolving in time, as typically des…
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…
MTLComb: multi-task learning combining regression and classification tasks for joint feature selection
Han Cao, Sivanesan Rajan, Bianka Hahn +4
Multi-task learning (MTL) is a learning paradigm that enables the simultaneous training of multiple communicating algorithms. Although MTL has been successfully applied to ether re…
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…
Bifurcations and loss jumps in RNN training
Lukas Eisenmann, Zahra Monfared, Niclas Alexander Göring +1
Recurrent neural networks (RNNs) are popular machine learning tools for modeling and forecasting sequential data and for inferring dynamical systems (DS) from observed time series.…