One-shot learning for the complex dynamical behaviors of weakly nonlinear forced oscillators
Not on the map. Why?
Abstract (arXiv)
Extrapolative prediction of complex nonlinear dynamics remains a central challenge in engineering. This study proposes a one-shot learning method to identify global frequency-response curves from a single excitation time history by learning governing equations. We introduce MEv-SINDy (Multi-frequency Evolutionary Sparse Identification of Nonlinear Dynamics) to infer the governing equations of non-autonomous and multi-frequency systems. The methodology leverages the Generalized Harmonic Balance (GHB) method to decompose complex forced responses into a set of slow-varying evolution equations. We validated the capabilities of MEv-SINDy on two critical Micro-Electro-Mechanical Systems (MEMS). These applications include a nonlinear beam resonator and a MEMS micromirror. Our results show that the model trained on a single point accurately predicts softening/hardening effects and jump phenomena across a wide range of excitation levels. This approach significantly reduces the data acquisition burden for the characterization and design of nonlinear microsystems.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 55 references that are in the map. First posted on arXiv 16 Apr 2026.
Builds on
6 of 55 references.
References · 6
- 2020Physics-informed learning of governing equations from scarce datacited by 167
- 2018Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systemscited by 54
- 2018Data-driven Discovery of Cyber-Physical Systemscited by 13
- 2024Dynamical System Identification, Model Selection and Model Uncertainty Quantification by Bayesian Inferencecited by 9
- 2024Discovering physical laws with parallel symbolic enumerationcited by 9
- 2020Physics-based polynomial neural networks for one-shot learning of dynamical systems from one or a few samplescited by 2
Built on by
Not cited yet.