The paper presents an end-to-end digital twin solution for predicting the state of an aircraft structure. The method extends Gaussian processes to include derivative data for improved accuracy and uses a streaming sparse Cholesky factorization for efficient updating with in-service data. This enables real-time high-fidelity forecasting.
Demonstrates a practical approach to updating surrogate models with in-service data, which is critical for accurate forecasting of physical asset states in digital twins.
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