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Digital Twin

Data-driven transient forecasting for generator digital twin decision support

US & West ·

A calibrated surrogate model for forecasting generator transients was developed to support digital twin decision-making. The model evaluates planned load commands before application using event-conditioned Hankel-DMDc and split-conformal calibration for prediction bands, helping operators ensure voltage and frequency remain within limits.

Why it matters

This approach improves reliability of operational power system management by providing probabilistic assessment of control actions.

Relevant to DT products

Original headline
Data-Driven Generator Transient Prediction for Digital Twin Decision Support
Read the original: arXiv API — полнотекстовый поиск "digital twin"

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Related

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Digital twin speeds up reinforcement learning for lab robots

TwinBridge builds digital twins from 3D Gaussian splatting and physics simulation to supply policy initialization and dense visual rewards for real-world online learning of a laboratory robot. Simulator task events and geometric progress yield rewards for pretraining, distilled into a visual reward model and combined with execution history during human-in-the-loop refinement.

arXiv API — полнотекстовый поиск "digital twin" ↗
Digital twinUS & West

Dataset for deciding between evidence gathering and action in control-system incidents

The authors present TE-GvA, a counterfactual decision dataset for fault-driven incident response on the revised Tennessee Eastman process: 9,908 decision points from 1,990 simulated incidents, each with 4–35 candidate actions labelled via branched rollouts. With labels computed under the true hidden cause, gathering evidence is the best choice in only 1.1–1.3% of decisions.

DTEDT.ForecastarXiv — Computational Engineering, Finance, and Science (cs.CE) ↗