A discontinuous Galerkin level set formulation applied to the modelling of deformation patterns in multi-material viscous geological flow
Bibliographic record
Abstract
We formulate a numerical framework to model the structural patterns emerged from the long-term highly viscous tectonic flow for both two and three spatial dimensions by coupling the discontinuous Galerkin level set method with a finite element Stokes-like flow solver. Our formulation, implemented with adaptive mesh refinement near the material interface, allows for accurate interface capturing and automatic handling of topological splitting and merging. Compared to particle-in-cell family of methods, the level set formulation has the advantage of retaining information on the interface geometry, less memory requirement and the savings of computational expense on the two-way particle-mesh information transfer. Furthermore, our formulation discretizes the level set in the same finite element framework as the flow solver, thus enabling us to fully exploit the advantages of the finite element method such as the flexibility of mesh geometry and the ease of handling anisotropic materials. In order to track the finite deformation in the modelling domain, passive tracer particles are generated at and around locations of interest, whose deformation can be accumulated through arbitrary time interval within the total modelled time span, thus offering a fully dynamical approach for modelling non-steady and inhomogeneous structural patterns. The material distribution and the finite deformation pattern generated from the numerical model can be directly compared with the geological map patterns and the field structural analyses, thus offering the possibility of ground-truthing the modelling results by field evidence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".