Multiscale Modeling of Granular Materials Using Mesoscale DEM and Machine Learning Approaches
Bibliographic record
Abstract
Abstract We establish the necessary framework for inputting any kind of mesostructure into multi-scale models for granular materials. Keeping intact the general statistical homogenization scheme, we propose a strategy to compute the mechanical response of the mesostructures directly with discrete element simulations of a few grains or thanks to surrogate models relying on artificial neuron networks (ANN). By applying machine learning techniques at the mesoscale (instead of the Representative Elementary Volume scale), it is indeed possible to generate the necessary learning database from discrete element simulations at a relatively cheap computational cost. We apply the meso-DEM and meso-ANN strategies to the H-model (one particular micromechanical model), and we show that they can replicate the original analytical expression of the model on biaxial tests. This work paves the way for using more complex mesostructures to account for instance for gap-graded materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".