Efficient Nonlinear Multiscale Analysis Using Sparse Sampling-Based Model Order Reduction Method
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Bibliographic record
Abstract
In this study, we conducted to improve the computational efficiency of the classical FE2 method by introducing micro-level reduced order modeling technique. For the classical FE2 method, multiple repetitive computations in microscopic representative volume element are required considering nonlinearities of such unit cells. Therefore, a great amount of computational resource is required for the multiscale analysis considering the nonlinearities in both macro- and microscopic domains. We propose to introduce reduced-order modeling of the representative volume element model using sparse sampling-based nonlinear reduced-order modeling to improve the efficiency of FE2 analysis. We verify the proposed method comparing accuracy and efficiency with those of full FE2 analysis investigating several microscopic and associated macroscopic models.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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