Variational Digital Twins

• URL: http://arxiv.org/abs/2507.01047v1
• Date: Wed, 25 Jun 2025 02:05:30 GMT
• Title: Variational Digital Twins
• Authors: Logan A. Burnett, Umme Mahbuba Nabila, Majdi I. Radaideh,
• Abstract summary: We propose a variational digital twin (VDT) framework that augments standard neural architectures with a single Bayesian output layer.<n>This lightweight addition, along with a novel VDT updating algorithm, lets a twin update in seconds on commodity GPU.<n>The VDT is evaluated on four energy-sector problems.
• Score: 0.44241702149260353
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: While digital twins (DT) hold promise for providing real-time insights into complex energy assets, much of the current literature either does not offer a clear framework for information exchange between the model and the asset, lacks key features needed for real-time implementation, or gives limited attention to model uncertainty. Here, we aim to solve these gaps by proposing a variational digital twin (VDT) framework that augments standard neural architectures with a single Bayesian output layer. This lightweight addition, along with a novel VDT updating algorithm, lets a twin update in seconds on commodity GPUs while producing calibrated uncertainty bounds that can inform experiment design, control algorithms, and model reliability. The VDT is evaluated on four energy-sector problems. For critical-heat-flux prediction, uncertainty-driven active learning reaches R2 = 0.98 using 47 % fewer experiments and one-third the training time of random sampling. A three-year renewable-generation twin maintains R2 > 0.95 for solar output and curbs error growth for volatile wind forecasts via monthly updates that process only one month of data at a time. A nuclear reactor transient cooldown twin reconstructs thermocouple signals with R2 > 0.99 and preserves accuracy after 50 % sensor loss, demonstrating robustness to degraded instrumentation. Finally, a physics-informed Li-ion battery twin, retrained after every ten discharges, lowers voltage mean-squared error by an order of magnitude relative to the best static model while adapting its credible intervals as the cell approaches end-of-life. These results demonstrate that combining modest Bayesian augmentation with efficient update schemes turns conventional surrogates into uncertainty-aware, data-efficient, and computationally tractable DTs, paving the way for dependable models across industrial and scientific energy systems.

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