Bibliographic record
Abstract
An engineering system may consist of several different types of components, belonging to such physical “domains” as mechanical, electrical, fluid, and thermal. It is termed a multi-domain (or multi-physics) system. In developing an analytical model of a multi-physics system, it is advantageous to use “unified” and “integrated” procedures for formulating different physical domains while including inter-domain dynamic interactions, in a systematic manner that will lead to a “unique” (single) model having physically meaningful variables. Such a model formulation is the focus of the present paper. In this context, a generalized method to incorporate impedance (e.g., the analogous use of mobility in the mechanical domain), exporting the modeling procedures from one domain into a different domain, conversion of a system in one domain into another domain, and realization of an equivalent single-domain model for a multi-domain system are addressed. This knowledge is useful in education, research, and application of multi-physics models of engineering dynamic systems. Illustrative examples are provided to clarify the presented approaches. This paper assumes a knowledge in linear graphs and some background material, as presented in the prior work of the author. The relevant nomenclature is listed at the end of the paper.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".