MS057 - Digital Twins for Ship and Offshore Structures
This MS will have an emphasis on enabling technologies for Digital Twins for Ships and Offshore Structures, where we adopt the following definition of a Digital Twin [1]:
A digital twin is defined as a virtual representation of a physical asset, or a process enabled through data and simulators for real-time prediction, optimization, monitoring, control, and decision-making.
To enable predictive twins, one may utilize Hybrid Analysis and Modelling (HAM) [2] that combines classical Physic-Based Methods (PBM) accelerated by means of Reduced Order Modelling (ROM) together with Data-Driven Methods (DDM) based on sensor measurement analysed by use of Machine Learning (ML). Pure Data-Driven Methods based on data (e.g. from sensor measurements) analysed by any means of AI is also welcome. In general, this MS welcome contributions on enabling technologies that can facilitate Predictive Digital Twins for Ships and Offshore Structures. Advanced applications of Predictive Digital Twins for Ships and Offshore Structures are also welcome.
A digital twin is defined as a virtual representation of a physical asset, or a process enabled through data and simulators for real-time prediction, optimization, monitoring, control, and decision-making.
To enable predictive twins, one may utilize Hybrid Analysis and Modelling (HAM) [2] that combines classical Physic-Based Methods (PBM) accelerated by means of Reduced Order Modelling (ROM) together with Data-Driven Methods (DDM) based on sensor measurement analysed by use of Machine Learning (ML). Pure Data-Driven Methods based on data (e.g. from sensor measurements) analysed by any means of AI is also welcome. In general, this MS welcome contributions on enabling technologies that can facilitate Predictive Digital Twins for Ships and Offshore Structures. Advanced applications of Predictive Digital Twins for Ships and Offshore Structures are also welcome.
