METAMODEL-BASED PROBABILISTIC DESIGN OPTIMIZATION OF STATIC SYSTEMS WITH AN EXTENSION TO DYNAMIC SYSTEMS
Bibliographic record
Abstract
In design, much research deals with cases where design variables are deterministic thus ignoring possible uncertainties present in manufacturing or environmental conditions. When uncertainty is considered, the design variables follow a particular distribution whose parameters are defined. Probabilistic design aims to reduce the probability of failure of a system by moving the distribution parameters of the design variables. The most popular method to estimate the probability of failure is a Monte Carlo Simulation where, using the distribution parameters, many runs are generated and the number of times the system does not meet specifications is counted. This method, however, can become time-consuming as the mechanistic model developed to model a physical system becomes increasingly complex. From structural reliability theory, the First Order Reliability Method (FORM) is an efficient method to estimate probability and efficiently moves the parameters to reduce failure probability. However, if the mechanistic model is too complex FORM becomes difficult to use. This paper presents a methodology to use approximating functions, called 'metamodels', with FORM to search for a design that minimizes the probability of failure. The method will be applied to three examples and the accuracy and speed of this metamodel-based probabilistic design method will be discussed. The speed and accuracy of three popular metamodels, the response surface model, the Radial Basis Function and the Kriging model are compared. Later, some theory will be presented on how the method can be applied to systems with a dynamic performance measure where the response lifetime is required to computer another performance measure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".