Explicit/multi-parametric model predictive control of dissipative distributed parameter systems
Bibliographic record
Abstract
This work focuses on the development of an explicit/multi-parametric model predictive control algorithm to stabilize the discrete infinite-dimensional system arising from the discrete state space modeling of certain class of dissipative distributed parameter systems, specifically, parabolic partial differential equation (PDE) systems. In particular, the class of parabolic PDEs that captures a large number of transport-reaction systems yields a discrete modal representation which captures the dominant dynamics of the parabolic PDE system. The proposed explicit/multi-parametric model predictive control algorithm is constructed in a way that the objective function is concerned with only the low-order modes, while the state constraints involve both the low-order and higher-order modes. The explicit model predictive control problem is solved off-line by dynamic programming and multi-parametric quadratic programming techniques, and the solution is expressed as a piecewise affine function with its corresponding critical regions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".