Canonical and noncanonical Hamiltonian operator inference

Gruber, Anthony David; Tezaur, Irina Kalashnikova

doi:10.1016/j.cma.2023.116334

Title: Canonical and noncanonical Hamiltonian operator inference

Journal Article · 28 August 2023 · Computer Methods in Applied Mechanics and Engineering

DOI: https://doi.org/10.1016/j.cma.2023.116334 · OSTI ID:2311488

^[1]; Tezaur, Irina Kalashnikova ^[2]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research
Sandia National Lab. (SNL-CA), Livermore, CA (United States)

Here, 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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Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)

Grant/Contract Number:: NA0003525

OSTI ID:: 2311488

Report Number(s):: SAND--2023-11102J

Journal Information:: Computer Methods in Applied Mechanics and Engineering, Journal Name: Computer Methods in Applied Mechanics and Engineering Vol. 416; ISSN 0045-7825

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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