Physics-based vs data-driven constitutive modeling of granular media down an inclined plane
Bibliographic record
Abstract
We present a numerical study of 3D granular flow down an inclined plane, using Discrete Element Method (DEM). The data of individual grains are used to compute the macroscopic density, velocity, and stress fields through a coarse-graining technique (CG). We begin by analyzing granular flows with the analytical rheology model μ( I ), which has proven to be effective in describing dense, quasistatic, and inertial flow regimes. We also present a data-driven approach that utilizes machine learning methods to build constitutive models. This approach does not rely on predetermined balance equations; instead, the resulting constitutive model is trained directly on DEM-CG data to learn patterns and relationships. In general, our results suggest the potential of ML approaches in predicting stress distributions in dense granular flows. As expected, random forest and neural network analysis are more effective compared to simple linear regression. In particular, neural networks appear as a promising avenue for advancing predictive accuracy in future studies.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".