Sensitivity Analysis Based on the Fisher Information Matrix Applied to Systems With Random Design Inputs
Bibliographic record
Abstract
Abstract Uncertainties in the parameters adopted during the design process make it challenging to design against the reliability of an engineering system. The identification of parameters that are more sensitive to uncertainties is carried out by a sensitivity analysis of the distribution of the output variables. In this context, we have explored the relation between the Fisher information matrix (FIM) and the design entropy, to develop a framework to analyze the degree of change of the probability of failure and entropy as a result of the variation of input parameters. It is found that the changes in the entropy and probability of failure, associated to the variation of the parameters of the distribution of the input variables, are linear combinations of the eigenvalues of the FIM and the projections of the eigenvectors onto the sensitivity vectors, respectively. As an application, the FIM-based sensitivity analysis is performed from Monte Carlo simulations in a physical dynamic structure subjected to random design parameters drawn from Gaussian and Gamma distributions.
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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 | 0.019 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".