ARC4CFD: Learning how to leverage High-Performance Computing with Computational Fluid Dynamics
Bibliographic record
Abstract
Computational Fluid Dynamics (CFD) is a field of computational physics that relies heavily on modern Advanced Research Computing (ARC) resources (Cant, 2002).The spatial and temporal resolutions required to solve modern CFD problems means that they can take advantage of the full benefits of large-scale distributed-memory parallelization that is available on high-performance computing (HPC) systems on ARC infrastructure.The CFD user base is broad, diverse and interdisciplinary.As CFD tools have progressed over the past decades, the improved robustness, predictive capabilities, and user-friendliness led them to be adopted by nontraditional HPC users such as new graduate students, experimentalists, theoreticians, and student design teams.Advanced Research Computing for Computational Fluid Dynamics, or ARC4CFD, is an open source, asynchronous online course (https://arc4cfd.github.io) that was developed to give users a basic understanding of fluid dynamics and CFD to bridge the knowledge gap toward an effective use of CFD on modern ARC resources.
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.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.032 |
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".