Combined Excitation and System Parameter Identification of Dynamic Systems by an Inverse Meta-Model
Why this work is in the frame
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Bibliographic record
Abstract
In the inverse problem, it is common that either the corresponding component parameters or the corresponding input signals are obtained for a given output or response. Most model-based solutions to the inverse problem involve optimization using the so-called forward model. The forward model typically comprises the mechanistic model in some form. Most commonly, inverse problems are formulated in a static setting where a wealth of theoretical results and numerical methods are available. However, there are many important dynamic applications wherein time-dependent information needs to be discerned from time-dependent data. Recently, data-based approaches, or model-free methods, have been invoked whereby feature extraction methods such as Support vector machines (SVM) and artificial neural networks (ANN) are used. Herein we develop an inverse solution for dynamic systems through easy-to-understand least-squares meta-model mathematics. The input and output training data are interchanged, so that a mixed input comprising both component parameters and discrete-time excitations can be found for a given discrete-time output. Single-value decomposition (SVD) makes any matrix inversion tractable. The inverse meta-model is compared to the optimization method and ANN using mechanistic models for fidelity, and is shown to have better accuracy and much increased speed.
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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 | 0.001 | 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