Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds

Harsh Sharma* (Corresponding Author), Hongliang Mu, Patrick Buchfink, Rudy Geelen, Silke Glas, Boris Kramer

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
4 Downloads (Pure)


This work presents two novel approaches for the symplectic model reduction of high-dimensional Hamiltonian systems using data-driven quadratic manifolds. Classical symplectic model reduction approaches employ linear symplectic subspaces for representing the high-dimensional system states in a reduced-dimensional coordinate system. While these approximations respect the symplectic nature of Hamiltonian systems, linear basis approximations can suffer from slowly decaying Kolmogorov N- width, especially in wave-type problems, which then requires a large basis size. We propose two different model reduction methods based on recently developed quadratic manifolds, each presenting its own advantages and limitations. The addition of quadratic terms to the state approximation, which sits at the heart of the proposed methodologies, enables us to better represent intrinsic low- dimensionality in the problem at hand. Both approaches are effective for issuing predictions in settings well outside the range of their training data while providing more accurate solutions than the linear symplectic reduced-order models.
Original languageEnglish
Article number116402
Number of pages23
JournalComputer methods in applied mechanics and engineering
Issue numberPart A
Early online date8 Sept 2023
Publication statusPublished - 1 Dec 2023


  • Symplectic model reduction
  • Hamiltonian systems
  • Data-driven modeling
  • Quadratic manifolds
  • Scientific machine learning


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