Canonical and Noncanonical Hamiltonian Operator Inference

Anthony Gruber, Irina Tezaur
4/13/2023
cs.LGmath.DS

No official code found. Best implementation: sandialabs/HamiltonianOpInf (3 stars, last commit 2025-05-19).

Abstract

A method for the nonintrusive and structure-preserving model reduction of canonical and noncanonical Hamiltonian systems is presented. Based on the idea of operator inference, this technique is provably convergent and reduces to a straightforward linear solve given snapshot data and gray-box knowledge of the system Hamiltonian. Examples involving several hyperbolic partial differential equations show that the proposed method yields reduced models which, in addition to being accurate and stable with respect to the addition of basis modes, preserve conserved quantities well outside the range of their training data.

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Code Implementations(1)

sandialabs/HamiltonianOpInfCommunity100%

This repo contains files for reproducing results in the following paper:Canonical and Noncanonical Hamiltonian Operator Inference

32SWIG, MATLABMar 25, 20231 years ago
scr-2917snl-data-analysis

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Cite this paper

@article{gruber2023canonical,
  title  = {Canonical and Noncanonical Hamiltonian Operator Inference},
  author = {Anthony Gruber and Irina Tezaur},
  year   = {2023},
  eprint = {2304.06262},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.06262v2}
}

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