ArticleNature communications2022
Data-driven modeling and prediction of non-linearizable dynamics via spectral submanifolds.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- Nonlinear spectral modeling and control of soft-robotic muscles from data.Nature communications · 2026Article
- Data-driven reduced modeling of neural dynamics.Nature communications · 2026Article
- Article
- Globalizing manifold-based reduced models for equations and data.Nature communications · 2025Article
- Personalized prediction of gait freezing using dynamic mode decomposition.Scientific reports · 2025Article
- Precision non-invasive brain stimulation: anJournal of neural engineering · 2025Article
- Finite element-based nonlinear dynamic optimization of nanomechanical resonators.Microsystems & nanoengineering · 2025Article
- Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks.Nature communications · 2024Article
- Data-driven linearization of dynamical systems.Nonlinear dynamics · 2024Article
- Data-driven nonlinear model reduction to spectral submanifolds in mechanical systems.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Data-Driven Identification of Rational Nonlinear Dynamics in Biochemical Networks via an Implicit Singular Value Decomposition Based Framework.IET systems biologyArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We develop a methodology to construct low-dimensional predictive models from data sets representing essentially nonlinear (or non-linearizable) dynamical systems with a hyperbolic linear part that are subject to external forcing with finitely many frequencies. Our data-driven, sparse, nonlinear models are obtained as extended normal forms of the reduced dynamics on low-dimensional, attracting spectral submanifolds (SSMs) of the dynamical system. We illustrate the power of data-driven SSM reduction on high-dimensional numerical data sets and experimental measurements involving beam oscillations, vortex shedding and sloshing in a water tank. We find that SSM reduction trained on unforced data also predicts nonlinear response accurately under additional external forcing.
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Registered trials
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