Each component is represented by partial differential equations and at high fidelity, finite element methods and a computational mesh are used to determine the impact of flight on each segment, generating physics-based training data that feeds into a machine learning classifier.
The combination of model reduction and decomposition made the digital twin 1000 times faster than other methods, cutting simulation times from hours or minutes to seconds while maintaining the accuracy needed for decision-making.
"The method is highly interpretable," she said. "I can go back and see what sensor is contributing to being classified into a state." The process naturally lends itself to sensor selection and to determining where sensors need to be placed to capture details critical to the health and safety of the UAV.