The main concerns of dynamic performance in stochastic finite element analysis (SFEA) are accurately capturing a system's probabilistic behavior subjected to random input parameters, such as material properties, loading conditions, and geometric uncertainties. Specifically, the focus is on analyzing the system's dynamic response under stochastic loads or uncertainties, including its natural frequencies, mode shapes, and transient behavior. To support informed decision-making, the goal is to provide reliable predictions of the system's dynamic performance, including response variability and probability distribution.

Digital twins can be combined with SFEA to improve the accuracy of dynamic performance predictions. A digital twin is a virtual representation of a physical system that integrates data from various sources, including sensor measurements, simulation models, and historical performance data. By combining SFEA with digital twins, we can obtain real-time feedback on the system's actual behavior and update the model parameters accordingly, leading to improved predictions of the system's dynamic performance. Additionally, digital twins can be used to optimize the system's design, identify potential failure modes, and develop effective maintenance strategies.

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