How to Compute Invariant Manifolds and their Reduced Dynamics in High-Dimensional Finite-Element Models
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Abstract (arXiv)
Invariant manifolds are important constructs for the quantitative and qualitative understanding of nonlinear phenomena in dynamical systems. In nonlinear damped mechanical systems, for instance, spectral submanifolds have emerged as useful tools for the computation of forced response curves, backbone curves, detached resonance curves (isolas) via exact reduced-order models. For conservative nonlinear mechanical systems, Lyapunov subcenter manifolds and their reduced dynamics provide a way to identify nonlinear amplitude-frequency relationships in the form of conservative backbone curves. Despite these powerful predictions offered by invariant manifolds, their use has largely been limited to low-dimensional academic examples. This is because several challenges render their computation unfeasible for realistic engineering structures described by finite-element models. In this work, we address these computational challenges and develop methods for computing invariant manifolds and their reduced dynamics in very high-dimensional nonlinear systems arising from spatial discretization of the governing partial differential equations. We illustrate our computational algorithms on finite-element models of mechanical structures that range from a simple beam containing tens of degrees of freedom to an aircraft wing containing more than a hundred-thousand degrees of freedom.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Mar 2021.
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Citing papers per year: 2021: 1; 2022: 4; 2023: 0; 2024: 0; 2025: 2; 2026: 1.
8 papers.
- 2022Data-Driven Modeling and Prediction of Non-Linearizable Dynamics via Spectral Submanifoldscited by 25
- 2021Data-driven Nonlinear Model Reduction to Spectral Submanifolds in Mechanical Systemscited by 3
- 2022Learning effective dynamics from data-driven stochastic systemscited by 3
- 2022Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approachescited by 1
- 2022Information Topologycited by 0
- 2025Revealing hidden correlations from complex spatial distributions: Adjacent Correlation Analysiscited by 0
- 2025Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparisoncited by 0
- 2026Physics-informed reduced-order modelling with equivariant spectral submanifoldscited by 0
- 2026Physics-informed reduced-order modelling with equivariant spectral submanifolds4 Aug 2026
- 2025Nonlinear Model Order Reduction of Dynamical Systems in Process Engineering: Review and Comparison15 Jun 2025
- 2025Revealing hidden correlations from complex spatial distributions: Adjacent Correlation Analysis6 Jun 2025
- 2022Information Topology7 Oct 2022
- 2022Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches12 May 2022
- 2022Learning effective dynamics from data-driven stochastic systems9 May 2022
- 2022Data-Driven Modeling and Prediction of Non-Linearizable Dynamics via Spectral Submanifolds13 Jan 2022
- 2021Data-driven Nonlinear Model Reduction to Spectral Submanifolds in Mechanical Systems5 Oct 2021