Multibody Systems (MBS) dynamics is a very active research area in Mechanical Engineering. MBS methods can be applied to many different engineering problems. While a large number of MBS formulations have already been published in the literature, new ones keep on being proposed and used in the solution of complex industrial and research applications. The recent developments in data-driven methods have made it possible to approach a wide variety of MBS problems, ranging from system identification to decision making, in novel ways.
This Special Issue intends to gather a series of contributions that reflect state-of-the-art applications of data-driven strategies to MBS problems and applications. The scope of this Issue spans recent developments in all areas of data-driven methods, including, but not limited to,
- reduced-order models,
- parameter and system identification,
- machine learning-based methods,
- neural networks,
- data-driven modelling and control,
- generative models,
- digital twins,
- and artificial intelligence.
Papers that discuss the role of MBS dynamics in emergent areas are of particular interest. Review papers and papers that present comparative studies are welcome, too.
