Updating simulation model parameters using stochastic gradient descent
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a method to automatically improve simulation model accuracy by using a stochastic gradient descent algorithm. The proposed algorithm updates models' parameters based on data collected from the actual domain under investigation. Collected data and feedback are fed into the simulation model to get predictions. In a linear prediction model, this data, along with the predictions, would form a typical regression problem; however, the stochastic gradient descent algorithm was modified to update the simulation model parameters. A tunneling case study is presented here, and the results show that the proposed algorithm can decrease simulation error by more than 50%, even in the case of incomplete simulation models or a missing inter-relationship between elements. Besides improving initial models, this paper provides a new approach for achieving data-driven simulation models that are updated in real time based on feedback from the actual domain. • Proposing a data-driven approach to enhance simulation performance. • Using stochastic gradient descent algorithm to update simulation parameters. • Deriving mathematical model to calculate error rate from a simulation model. • Applying the proposed method in a real case study of a tunneling project. • Suggesting a new approach for fitting data to model a simulation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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