Data-Driven Analysis of Nonlinear Vibrations
A special issue of Vibration (ISSN 2571-631X).
Deadline for manuscript submissions: closed (15 December 2023) | Viewed by 548
Special Issue Editors
Interests: structural dynamics; nonlinear vibrations (reduced-order modelling, analytical approximation, bifurcation analysis); fluid-structure interactions (flow-induced vibration, aeroelasticity); machine learning
Special Issue Information
Dear Colleagues,
In recent years, the increased demand placed on engineering structures has resulted in a marked interest in complex vibration problems, including nonlinear vibrations, fluid–structure interaction, aeroelasticity, digital twins, meta-structures, and a broad range of additional multiphysics problems. The importance of properly understanding and modelling this behavior can be observed, for example, in the recent in-flight break-up of an Airbus Zephyr unmanned air vehicle (2019). This complexity manifests not only in the behavioral response of the system, but also in the corresponding equations and models. As such, engineers often look to model reduction techniques or other approximation methods to accurately capture the behavior in a more practical model.
The modelling of such systems increasingly utilizes recent advancements in data science and machine learning as a method of training a model based on realistic simulation data, improving their practicability, and reducing the associated uncertainty. This Special Issue focuses on the most recent advancements in the field of data-driven dynamic modelling, collating the findings and approaches of the broad range of complex vibration problems outlined above. In doing so, the Special Issue will provide a comprehensive overview of this exciting area, so that insight can be applied in the engineering design process, as well as in further research.
Dr. Alexander Elliott
Dr. Andrea Cammarano
Guest Editors
Manuscript Submission Information
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Keywords
- nonlinear vibrations
- fluid-structure interaction
- aeroelasticity
- data-driven engineering
- physics-informed neural networks
- deep/machine learning
- multiphysics systems
- reduced-order modelling
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