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Author

J. Kutz

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Open access Sep 2026

Shallow recurrent decoders for neural and behavioural dynamics

A SHallow REcurrent Decoder (SHRED) architecture for mapping the dynamics of population codes to individual neurons and other proxy measures of neural activity and behaviour and empirically demonstrate the capabilities of the method on a number of model organisms including Caenorhabditis elegans, mouse, zebrafish and h...

A. Rude, J. Kutz · 0 citations
#machine learning Open access Oct 2025

Constrained Sensing and Reliable State Estimation with Shallow Recurrent Decoders on a TRIGA Mark II Reactor

Shallow Recurrent Decoder networks are a novel data-driven methodology able to provide accurate state estimation in engineering systems, such as nuclear reactors. This deep learning architecture is a robust technique designed to map the temporal trajectories of a few sparse measures to the full state space, including u...

S. Riva, C. Introini, J. Kutz et al. · 4 citations
Preprint Aug 2026

Stable Takens'Embedding Theorem for Non-Uniformly-Sampled Linear Systems

Takens'time-delay embedding theorem provides conditions under which delay-coordinate maps, formed using uniformly-sampled time series of trajectories evolving on attractors of dynamical systems, can faithfully represent the dynamics of the original system. Nonlinear systems can be highly sensitive, and Takens'theorem d...

Fisher Ng, J. Kutz · 0 citations

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