A Quadratic Manifold for Model Order Reduction of Nonlinear Structural Dynamics
Not on the map. Why?
Abstract (arXiv)
This paper describes the use of a quadratic manifold for the model order reduction of structural dynamics problems featuring geometric nonlinearities. The manifold is tangent to a subspace spanned by the most relevant vibration modes, and its curvature is provided by modal derivatives obtained by sensitivity analysis of the eigenvalue problem, or its static approximation, along the vibration modes. The construction of the quadratic manifold requires minimal computational effort once the vibration modes are known. The reduced order model is then obtained by Galerkin projection, where the configuration-dependent tangent space of the manifold is used to project the discretized equations of motion.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Oct 2016.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 2.
5 papers.
- 2023Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamicscited by 3
- 2025Operator Inference Aware Quadratic Manifolds with Isotropic Reduced Coordinates for Nonintrusive Model Reductioncited by 2
- 2026Nonlinear model reduction for transport-dominated problemscited by 1
- 2025Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inferencecited by 0
- 2026Sparse POD Mode Selection and Manifold Dimensionality Reduction with Neural Networkscited by 0
- 2026Sparse POD Mode Selection and Manifold Dimensionality Reduction with Neural Networks26 May 2026
- 2026Nonlinear model reduction for transport-dominated problems1 Feb 2026
- 2025Active learning for data-driven reduced models of parametric differential systems with Bayesian operator inference30 Dec 2025
- 2025Operator Inference Aware Quadratic Manifolds with Isotropic Reduced Coordinates for Nonintrusive Model Reduction28 Jul 2025
- 2023Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics26 Aug 2023