A Survey of Modeling and Optimization Methods for Multi-Scale Heterogeneous Lattice Structures
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper aims to provide a comprehensive review of the state-of-the-art modeling and optimization methods for multi-scale heterogeneous lattice structures (MSHLS) to further facilitate the more design freedom. In this survey, a design process including optimization and modeling for MSHLS is proposed. Material composition and multi-scale geometric modeling methods for representation of material and geometry information are separately discussed. Moreover, the optimization methods including multi-scale and multi-material optimization design methods, as well as the simulation methods suitable for MSHLS are, respectively, reviewed. Finally, the relationship, advantages, and disadvantages of MSHLS modeling and optimization methods are summarized with discussion and comparison, which provides a guidance to further take advantage of MSHLS to improve the performance and multifunctional purpose of production for software developers and researchers concerning the design approaches and strategies currently available.
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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 | 0.001 | 0.001 |
| 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