Time-Variant Digital Twin Modeling through the Kalman-Generalized Sparse Identification of Nonlinear Dynamics
Why this work is in the frame
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Bibliographic record
Abstract
A digital twin is a computer-based digital representation that simulates the behavior of a physical system. Digital twins help users to interact with real-world processes digitally. Time-variant modeling is critical to preserving the accuracy of digital twin models as the process dynamics change with time. Kalman filter is a well-known recursive algorithm that adjusts the process state estimates using real-time measurements. Sparse identification of nonlinear dynamics (SINDy) is an algorithm that automatically identifies system models from large data sets using sparse regression so as to prevent overfitting and find an ideal trade-off between model complexity and accuracy. In this paper, the SINDy approach is first extended to the generalized SINDy (GSINDy). Then, the GSINDy is integrated with Kalman filter to automatically identify time-variant digital twin models for online applications. The effectiveness of the algorithm is revealed through a simulation example based on Lorenz system and an industrial diesel hydrotreating unit example.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it