We propose sparse identification of port-Hamiltonian systems (SIPHy), enabling structure-preserving symbolic regression from noisy trajectory observations. The method applies to port-Hamiltonian systems, which provide a general framework for describing dynamical systems in terms of energy exchange, dissipation and cont...
Håkon Noren Myhr, Sølve Eidnes, J. Kutz· 0 citations
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· Philosophical transactions o...· 0 citations
The Pretrained Estimator is proposed, a novel neural parameter estimation approach that embeds the physical equations into a neural network's training process to estimate RC parameters and achieves the best prediction performance among all RC-based methods and yields the lowest and least variable MPC costs across build...
F. Raisch, T. Germann, Sang-woo Ham et al.· 3 citations
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.· Chemical Engineering and Sci...· 4 citations
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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