Data-predictive Control of Multi-Timescale Nonlinear Processes
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
A novel big data-predictive control approach for nonlinear multi-timescale processes is presented in this paper. Multiple Dynamical Latent Variable Autoencoders (DLVAEs) are employed to approximate multi-timescale dynamics, utilizing timescale-based low-pass filtering and resampling of historical input-output data. The encoder in each DLVAE projects the nonlinear physical variable space onto a linear latent variable space, represented by a kernel space in behavioral system theory. During training, we not only impose kernel spaces and reconstruct data but also establish connections among latent variables from different DLVAEs at matching time-steps. Collectively, these multi-level latent variables span a wide prediction time horizon with limited (non-uniformly spaced) steps encompassing the current, near, and distant future. In online tracking control, we guide the latent variables from each DLVAE to their respective setpoints (derived from physical variable setpoints) while maintaining consistent physical variable values at matching time-steps, all within a linear framework.
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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