Real-time update of data-driven reduced and full order models with applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We consider a dynamic mode decomposition (DMD) based technique to identify data-driven reduced-order and full-order models and propose two approaches to update them in real-time. These updates are crucial for the models to adapt to the evolving process. The proposed approaches function by calculating the update of the singular value decomposition (SVD), which is the core operation in DMD. In particular, two approaches involving temporal updates and additive modifications are used to update the SVDs. Further, the equivalence of both approaches is proved under special rank conditions. Also, the computational costs involved in these approaches are discussed. The technique is well suited for adaptive process modeling that can be exploited for real-time process monitoring, estimation, control, and optimization. The efficacy of the proposed approach is demonstrated using a large-scale benchmark wastewater treatment process .
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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