Causality-Augmented generalization network with cross-domain meta-learning for interlayer slipping recognition in viscoelastic sandwich structures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Accurate interlayer slipping recognition in viscoelastic sandwich structures (VSSs) is critical for mechanical equipment’s safety and reliability. However, significant domain shifts exist in VSSs data under variable working conditions, and domain data under certain conditions cannot be directly accessed during training. This renders conventional domain adaptation methods ineffective. To address the problems, we proposed causality-augmented generalization network (CGN) without accessing target domains for VSSs’ slipping recognition. CGN comprises a swin-transformer feature extractor and a capsule network classifier with an FC decoder. The feature extractor aims to fully extract discriminative features of VSSs data and promote their domain invariance across multiple domains. Building on this foundation, the classifier further extracts the underlying causal features associated with the labels and performs slipping recognition, thereby enhancing the model’s generalization and stability across various domains. The decoder serves as a regularizer to assist in learning meaningful representations of input data. Moreover, cross-domain meta -learning strategy is incorporated into the generalized training process to further strengthen the model’s generalization ability. The experiments on VSSs’ cross-domain datasets illustrate that CGN can be trained on some domains and directly tested on multiple unknown domains with desirable results, showing its effective generalization and stability for slipping recognition.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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