mlsanityData to 7 Oct 2026

Pseudo-Hamiltonian system identification

arXiv 2305.06920 ↗First posted 9 May 2023eess.SY cs.LG cs.NAcited by 1

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Abstract (arXiv)

Identifying the underlying dynamics of physical systems can be challenging when only provided with observational data. In this work, we consider systems that can be modelled as first-order ordinary differential equations. By assuming a certain pseudo-Hamiltonian formulation, we are able to learn the analytic terms of internal dynamics even if the model is trained on data where the system is affected by unknown damping and external disturbances. In cases where it is difficult to find analytic terms for the disturbances, a hybrid model that uses a neural network to learn these can still accurately identify the dynamics of the system as if under ideal conditions. This makes the models applicable in some situations where other system identification models fail. Furthermore, we propose to use a fourth-order symmetric integration scheme in the loss function and avoid actual integration in the training, and demonstrate on varied examples how this leads to increased performance on noisy data.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 35 of its 65 references that are in the map. First posted on arXiv 9 May 2023.

Builds on

35 of 65 references.

References · 35

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23 more
  1. 2020SINDy-PI: A Robust Algorithm for Parallel Implicit Sparse Identification of Nonlinear Dynamicscited by 125
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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Coarse-Graining Hamiltonian Systems Using WSINDycited by 11