Calibration of DEM input parameters for simulation of the cohesive materials: Comparison of response surface method and machine learning models
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 methodology for calibrating discrete element method input parameters for simulating cohesive materials. The Plackett-Burman method was initially employed to identify the significant input parameters. Subsequently, the performances of response surface methodology (RSM), artificial neural networks (ANN), and random forest (RF) models for calibration were compared. The results demonstrated that the random forest model outperformed the two other models, achieving an RMSE of 1.89, an R-squared of 94 %, and an MAE of 1.63. The ANN model followed closely, with an RMSE of 3.12, an R-squared of 89 %, and an MAE of 2.18, while the RSM model exhibited lower performance with an RMSE of 6.84, an R-squared of 86 %, and an MAE of 5.41. This study presents a framework for enhancing the accuracy of DEM simulations. Finally, the robustness and adaptability of the calibration approach were demonstrated by applying calibrated parameters from one particle size to another. • Developed a novel method for calibration of DEM parameters of cohesive materials. • Used Plackett-Burman method to identify vital inputs needed for DEM calibration. • Compared performances of three calibration models: RSM, ANN, and RF. • Verified RF model's performance consistency across various particle sizes. • Emphasized the potential of using machine learning methods in the calibration of DEM.
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 | 0.000 | 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