From the 1 of 4 linked papers with an AI index.
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A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems
Christoph Jürgen Hemmer, Florian Plaswig, Daniel Durstewitz
The paper introduces DynaBase, a two‑parameter, interpretable model that can reconstruct dynamical systems in a zero‑shot setting by linearly blending the current latent state with…
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…