Neural Network Bushing Model Development Using Simulation
Why this work is in the frame
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Bibliographic record
Abstract
Neural networks are computationally efficient mathematical models that can be used to model quantitative and qualitative data. A neural network can be created through training with known input and output load-deflection data such that it learns to generalize the material characteristics without over-predicting the training data and losing its ability to anticipate behavior outside the training set. The challenge in creating a neural network model of a rubber bushing in a virtual model of a prototype assembly, for instance, is the lack of a physical prototype assembly. This paper describes a method by which data can be measured from a virtual prototype and used to define an appropriate data acquisition for the physical bushing. Training data can then be acquired using these guidelines and used for neural network model development. Subsequently, the enhanced model can then be used in the virtual simulation environment to increase the accuracy of the simulation results.
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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