A relative adequacy framework for multi-model management in single- and multidisciplinary design optimization
Bibliographic record
Abstract
We present a novel multi-model management method for numerical design optimization.The goal is to determine whether any of the analysis models associated with lower computational cost (that are typically expected to have inferior predictive capability relative to models associated with higher computational cost) can be used in certain areas of the design space as the latter is being explored during the optimization process.The framework quantifies and utilizes relative errors among available models regardless of their expected fidelity to enhance the predictive capability of inexpensive models and reduce the use of expensive ones.We implement our strategy by means of a trust-region management framework that utilizes the mesh adaptive direct search derivative-free optimization algorithm.We first present the methodology for single-disciplinary design optimization problems and demonstrate it using a cantilevered flexible beam example.Results show significant reduction in the computational cost of the optimization process.We also investigate the scalability of the proposed method using an airfoil shape optimization problem.We then proceed to extend our method to solve multidisciplinary design optimization problems with particular emphasis on strongly-coupled fluid-structure interaction.We illustrate that interactions can have a significant impact on multi-model management as models that could have been selected in a single-disciplinary analysis environment can be inadequate in a multidisciplinary analysis context.We implement our method for two multidisciplinary design optimization architectures: the monolithic multidisciplinary feasible formulation (also known as all-at-once) and a penalty-based distributed interdisciplinary feasible formulation.Finally, we extend the method to allow the consideration of time-dependent multidisciplinary design optimization problems.The algorithms are modified to automate the search for adequate modeling parameters during the time-dependent multidisciplinary analysis.We demonstrate the proposed time-invariant and time-dependent multidisciplinary design optimization methods by means of three problems: a flexible plate fluid-structure interaction problem, a flexible beam fluid-structure interaction problem, and a transonic fan flow problem.Results show that both methods are accurate and efficient and result in significant cost savings, especially in the presence of strongly-coupled disciplines.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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; both teacher heads agree on what is shown here.
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".