2D_ElastoPlastoDynamics: 2D dynamic non-linear structural mechanics dataset, with a non-linear non-local constitutive law
Bibliographic record
Abstract
This dataset contains 2D dynamic non-linear structural mechanics with a non-linear non-local constitutive law. The files format is PLAID, see the plaid documentation. The variablity in the samples is the geometry (mesh). Outputs of interest are 3 transient fields: x and y components of the displacement at the nodes and the erosion status at the elements. The dataset has a training set of size 1000 and a testing set of size 18. Outputs are not provided on the testing sets. Tips to access the data: After decompressing the downloaded file: from plaid.containers.dataset import Datasetfrom plaid.problem_definition import ProblemDefinition dataset = Dataset()problem = ProblemDefinition() problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True) print("problem =", problem)print("dataset =", dataset) ids_train = problem.get_split('train') ids_test = problem.get_split('test') sample_train_0 = dataset[ids_train[0]] sample_test_0 = dataset[ids_test[0]] print(sample_train_0) print(sample_test_0) sample = sample_train_0 # inputs mesh = sample.get_mesh() mesh = sample.get_mesh(time=0.01, apply_links=True, in_memory=True)# links to mesh at time=0 since mesh is constant print(mesh) # outputs for fn in ["U_x", "U_y"]: field = sample.get_field(fn, time=0.01) print(field) field = sample.get_field("EROSION_STATUS", location="CellCenter", time=0.01) print(field)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.064 |
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".