dynoGP: Deep Gaussian Processes for dynamic system identification

Alessio Benavoli, Dario Piga, Marco Forgione, Marco Zaffalon
2/8/2025
stat.MLcs.LG

No official code found. Best implementation: Dynamic-Systems-and-GP/GPdyn (91 stars, GPL-2.0, last commit 2017-09-07).

Abstract

In this work, we present a novel approach to system identification for dynamical systems, based on a specific class of Deep Gaussian Processes (Deep GPs). These models are constructed by interconnecting linear dynamic GPs (equivalent to stochastic linear time-invariant dynamical systems) and static GPs (to model static nonlinearities). Our approach combines the strengths of data-driven methods, such as those based on neural network architectures, with the ability to output a probability distribution. This offers a more comprehensive framework for system identification that includes uncertainty quantification. Using both simulated and real-world data, we demonstrate the effectiveness of the proposed approach.

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Code Implementations(1)

Dynamic-Systems-and-GP/GPdynCommunity70%
GPL-2.0

Gaussian Process Model Dynamic System Identification Toolbox for Matlab

9132C, C++Jan 19, 20159 years agoGPL-2.0

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Cite this paper

@article{benavoli2025dynogp,
  title  = {dynoGP: Deep Gaussian Processes for dynamic system identification},
  author = {Alessio Benavoli and Dario Piga and Marco Forgione and Marco Zaffalon},
  year   = {2025},
  eprint = {2502.05620},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2502.05620v1}
}

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