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Variational digital twins
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AI
DOI：10.1016/j.egyai.2026.100756.png)
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Abstract
En 中文
• Introducing a fast-assimilation variational digital twin framework with uncertainty bounds. • Achieving high accuracy using fewer sensor data via active learning. • Robust temperature field reconstruction from limited sensor data. • Demonstrates robustness in battery, nuclear, and energy systems with commodity GPUs.
Keywords:
Digital twins
Variational inference
Power grid forecasting
Uncertainty quantification
Real-time energy modeling