mlsanityData to 7 Oct 2026

dynoGP: Deep Gaussian Processes for dynamic system identification

arXiv 2502.05620 ↗First posted 8 Feb 2025stat.ML cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 17 of its 42 references that are in the map. First posted on arXiv 8 Feb 2025.

Builds on

17 of 42 references.

References · 17

  1. 2017Deep Neural Networks as Gaussian Processescited by 617
  2. 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 603
  3. 2020HiPPO: Recurrent Memory with Optimal Polynomial Projectionscited by 600
  4. 2022On the Parameterization and Initialization of Diagonal State Space Modelscited by 418
  5. 2023Resurrecting Recurrent Neural Networks for Long Sequencescited by 397
  6. 2013Gaussian Processes for Big Datacited by 377
  7. 2022Diagonal State Spaces are as Effective as Structured State Spacescited by 350
  8. 2017Doubly Stochastic Variational Inference for Deep Gaussian Processescited by 143
  9. 2015Practical Bayesian model evaluation using leave-one-out cross-validation and WAICcited by 110
  10. 2015On the Error of Random Fourier Featurescited by 76
  11. 2017A general framework for Vecchia approximations of Gaussian processescited by 66
  12. 2020dynoNet: a neural network architecture for learning dynamical systemscited by 28
5 more
  1. 2019Model structures and fitting criteria for system identification with neural networkscited by 16
  2. 2020Deep Sigma Point Processescited by 16
  3. 2020Time series forecasting with Gaussian Processes needs priorscited by 13
  4. 2021Correlated Product of Experts for Sparse Gaussian Process Regressioncited by 7
  5. 2024Baseline Results for Selected Nonlinear System Identification Benchmarkscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Stable-by-Design Neural Network-Based LPV State-Space Models for System Identificationcited by 2
  2. 2026xFODE: An Explainable Fuzzy Additive ODE Framework for System Identificationcited by 0
  3. 2026Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identificationcited by 0