Applying Shapley Effect for Sensitivity Analysis During Reactor Transient
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
Sensitivity analysis is a critical tool in reactor safety assessments, as it evaluates the impact of uncertainties in input parameters, identifies key factors, and highlights potential safety risks and measures. Conventional sensitivity methods, such as Spearman, Pearson, or Kendall, while straightforward, are typically limited to linear relationships and independent input parameters. Shapley values offer a more advanced, model-agnostic approach to sensitivity analysis, making them particularly valuable in scenarios with dependent parameters or nonlinear systems.This study not only applies variance-based sensitivity methods, including Sobol indices and Shapley values, but also introduces the development of a reduced-order model (ROM) based on deep neural networks (DNNs) combined with Shapley values for time-dependent reactor simulations. This approach addresses the computational challenges of traditional methods, especially in cases involving correlated parameters, providing a more efficient and accurate sensitivity analysis. Sensitivity indices are calculated for the TWIGL benchmark, with two-group cross sections as the input parameters and core power during the ramp reactivity insertion transient as the output.The results demonstrate that Shapley values, combined with the DNN-based ROM, yield robust, accurate, and physically meaningful indices, especially in models with dependent parameters where Sobol indices may lead to over- or underestimation and might even result in negative indices. This highlights the advantages of Shapley values for comprehensive and reliable sensitivity analyses in complex reactor simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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