Structural Topology Optimization for Multiple Load Cases While Avoiding Local Minima
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Bibliographic record
Abstract
A new method for performing topology optimization while considering multiple load cases is presented. The technique is demonstrated using two classes of problems, the first of which seeks to minimize the maximum deflection under a series of fixed, point loads subject to a material volume constraint. The second is a classical weight minimization problem subject to constraints on the maximum deflection caused by each load case. Because the topology optimization problem involving SIMP materials is inherently non-convex, the optimized solution is highly sensitive to the starting point and search path followed during the optimization. Therefore, the proposed technique calls for the use of a composite objective function, which is defined as the Kreisselmeier–Steinhauser aggregate of the individual objectives corresponding to the different load cases. The technique is also applied to the weight minimization problem, in which case the KS function is used to construct an aggregate constraint function. In this way, sensitivity information from inactive load cases is taken into account throughout the optimization making the method less susceptible to local minima. Furthermore, by beginning with a low value for the
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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 | 0.000 |
| 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