HIERARCHICAL DOMAIN DECOMPOSITION WITH PARALLEL MESH REFINEMENT FOR BILLIONS-OF-DOF SCALE FINITE ELEMENT ANALYSES
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes a parallel fast generation method of large-scale meshes for a hierarchical domain decomposition method implemented in the open source parallel finite element software ADVENTURE. Since large-scale meshes need to be generated in order to perform various analyses in Japan's Petaflops Supercomputer, nicknamed the "K computer", a mesh refinement function and a communication table generation function without communication are newly developed and implemented for the hierarchical domain decomposition tool named ADVENTURE_Metis. The developed new version is named ADVENTURE_Metis Ver.2. Since a generation cost of a communication table for sending and receiving data among computational nodes becomes so expensive for the refined large-scale mesh, the present authors have newly developed a parallel algorithm such that the communication tables of vertices, edges and faces are updated each other during mesh refinement after the initial communication tables of vertices, edges and faces are generated for an initial mesh. As a result, the generation of a refined mesh model over billions degrees of freedom (DOFs) from an initial medium-size mesh model of about a million DOFs can be performed in a parallel computer in a short time.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it