Hybrid AI Analytics

HYB

Hybrid AI Analytics

Man looking at screen in officeThe purpose of this work package is to:

  • Develop robust, stable and explainable data-driven models for physical systems
  • Constrain models to enforce meaningful predictions
  • Transfer data-driven models from simulations to reality
  • Characterise and quantify uncertainty of data-driven models

This work package will develop methods to predict and reduce the uncertainty of data-driven models. The models will be constrained by existing knowledge, allowing to interpret the model (explainable AI) and reducing the amount of required training data. Applying this methods on real world applications will allow the industry partners to better predict the behavior of their facilities and improve their simulations, e.g. for condition monitoring, predictive maintenance, optimal utilization. 


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Short description: The aim of this project is to develop methodology for transfer learning for quantifying uncertainty from non-representative training data , i.e. how we can learn from simulated data and utilize this to quantify uncertainty in physical systems. Developed methodology will be tested in two usecases. 

Time perspective: 2020-2022 

Usecase 1:Virtual flow meter  

Involved partners:

Usecase 2: Predictive maintenance for wind turbines

Involved partners:

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Trønderenergi logo

 

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