Integrating Data-Driven Modeling with First-Principles Knowledge
Bibliographic record
Abstract
This Article addresses the problem of integrating subspace-based model identification with first-principles modeling for handling scenarios where the subspace model identifies spurious relationships between inputs and outputs. The key motivation is to suitably synergize the two approaches while retaining the simplicity of subspace-based model identification. In the proposed methodology, as is done with traditional subspace identification, state trajectories that best describe the input–output data are first computed (which implicitly correspond to an underlying linear time invariant model). In computing the system matrices using the state trajectories, constraints derived from first-principles understanding are incorporated into the optimization problem. To reconcile the resulting mismatch between the state trajectories and the system matrices, an iterative process is utilized. First, the system matrices computed from the optimization problem are utilized to re-estimate the state trajectories (this time utilizing a state estimator and the input and output trajectories). The state trajectories are, in turn, utilized to resolve the system matrices using the input–output data. The process is repeated until convergence occurs between successive state trajectories, thus yielding state trajectories and “consistent” system matrices. The efficacy of the proposed approach is shown via simulations using a nonlinear process example.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".