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Mastering Complex Modes: A New Method for Real-Time Modal Identification of Vibrating Systems
A novel algorithm for real-time modal identification in linear vibrating systems with complex modes is introduced, utilizing a combination of first order eigen-perturbation and second order separation techniques.
Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems
For improving the predictive capability of the underlying physics, we first use machine learning algorithm to learn a mapping between the estimated state and the input (model-form error) and then introduce it into the governing equation as an additional term.
Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system
In this paper, we propose a novel digital twin framework for stochastic nonlinear multi-degree of freedom (MDOF) dynamical systems.
