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Streaming Sparse Cholesky for Derivative-Informed Gaussian Process Surrogates in Digital Twins
US & West ·
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.
Investigates how many training pilots are needed when a digital twin of a wireless channel is used for channel estimation. The twin is treated as a complementary measurement fused with pilot observations. For the first time, this trade-off is formalized and quantified.
The article discusses an architecture of a digital twin for nuclear systems, integrating multiple models (physics-based and data-driven) to support decision making, state estimation, predictive control, and real-time data processing. The twin must synchronize with the physical facility faster than its operational cycle.
The company 'Aktiv' has finalized a document based on the foresight session held in Moscow in March 2025. The document includes strategic recommendations and forecasts for the industry development until 2040.
