Digital twinInternational
Streaming Sparse Cholesky for Derivative-Informed Gaussian Process Surrogates in Digital Twins
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.
Why it matters
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.
Relevant to DT products
- Original headline
- A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications
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