28 Jul Breaking Down and Building Up IoT’s Digital Twin
he digital twin is a federation of data and models that can be analyzed or put into a simulation to create useful information about the past, present or future of the DTs physical twin. The type of model and the level of model can make or break the analysis or simulation. In this episode of the IoT Business Show, I get down and dirty with Jim Tung, breaking the digital twin down in order to understand this most important tech at both the atomic and system level.
In this episode of the IoT Business Show, I get down and dirty with Jim Tung, breaking the digital twin down in order to understand this most important tech at both the atomic and system level.
Jim is a MathWorks Fellow, focused on business and technology strategy and works with key customers and partners. Jim has been with MathWorks for 28 years and has over 35 years of experience in real-time systems, data acquisition, and technical computing.
Model fidelity of the digital twin can be all the way down to the part or component level, or all the way up to the black box system level. Your level will likely be somewhere in between but the takeaway is to model the value you are creating with your IoT product at the right fidelity. The structure of your digital twin is synonymous with your data science strategy so it’s not only important to think model/data levels but also the hierarchical relationship between the models and data.
Here’s What We’ll Cover in this Episode
- The difference between data-driven models and functional models.
- Using raw data to calibrate, update and validate functional models.
- System level modeling.
- The importance model hierarchy to discover insights.
- A smart grid digital twin example.
- The role of deep learning in digital twin modeling.
- The actionable steps of how to create your digital twin.
Mentioned in this Episode and Other Useful Links
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