Mechanical System Modelling Using Physics-Based and Data-Based Subsystems
Bibliographic record
Abstract
Abstract Mechanical systems have been traditionally represented using parametric physics-based models. In this work, we introduce a novel concept, in this part of the mechanical system is represented using data-based subsystem models, and the overall mechanical system model is composed of these data-based and other, physics-based subsystems. A core element is the interfacing of the subsystems, which gives rise to interaction forces. The interfacing problem is formulated in a way that makes it possible to give a general representation to the interaction forces. We demonstrate that from the point of view of the physics-based subsystems the important element is that the data-based models can represent the interaction force systems properly. The data-based subsystems are developed using deep recurrent neural networks, and the training data is generated based on simulations using the fully parametric physics-based model of the system. Such training data could also be obtained through physical experimentation.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".