Transfer learning of recurrent neural network‐based plasticity models
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Simulation or modelingConsensus signal: Simulation or modeling
- Genre
- Candidate signal: MethodsConsensus signal: Methods
- Teacher disagreement score
- 0.308
- Threshold uncertainty score
- 0.739
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.318 · how far apart the two teachers sit on this one work
- Validation status
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
Abstract
Abstract Mechanics‐specific recurrent neural network (RNN) models are known for their ability to describe the complex three‐dimensional stress–strain response of elasto‐plastic solids for arbitrary loading paths. To apply RNN models to real materials, it is crucial to identify a strategy that allows for their training from small datasets that could be obtained from robot‐assisted experiments. It is demonstrated that regular training with datasets comprising random walks (RWs) in strain space yield a significantly higher generalization ability than the same number of sequences for smooth loading paths. Moreover, it is found that transfer learning, that is, initializing the weights and biases with the parameters from an already trained material, improves the convergence rates and reduces the required number of stress–strain sequences for training. When leveraging the experience gained for multiple materials through ensemble transfer learning, even more substantial improvements are obtained. For example, the same model accuracy and generalization ability is obtained from training with 400 smooth stress–strain sequences after ensemble transfer as from training with 10,000 RW sequences after regular training.
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.
The record
- Venue
- International Journal for Numerical Methods in Engineering
- Topic
- Non-Destructive Testing Techniques
- Field
- Engineering
- Canadian institutions
- Artificial Intelligence in Medicine (Canada)
- Funders
- not available
- Keywords
- InitializationGeneralizationTransfer of learningConvergence (economics)Computer scienceArtificial neural networkArtificial intelligenceStress (linguistics)Stress spacePlasticityTransfer (computing)Machine learningEnsemble learningSpace (punctuation)AlgorithmMathematicsFinite element methodStructural engineeringMaterials scienceEngineeringMathematical analysis
- Has abstract in OpenAlex
- yes