Model order reduction using neural network principal component analysis and generalized dimensional analysis
Bibliographic record
Abstract
Purpose This paper seeks to present a novel computational intelligence technique to generate concise neural network models for distributed dynamic systems. Design/methodology/approach The approach used in this paper is based on artificial neural network architectures that incorporate linear and nonlinear principal component analysis, combined with generalized dimensional analysis. Findings Neural network principal component analysis coupled with generalized dimensional analysis reduces input variable space by about 90 percent in the modeling of oil reservoirs. Once trained, the computation time is negligible and orders of magnitude faster than any traditional discretisation schemes such as fine‐mesh finite difference. Practical implications Finding the minimum number of input independent variables needed to characterize a system helps in extracting general rules about its behavior, and allows for quick setting of design guidelines, and particularly when evaluating changes in the physical properties of systems. Originality/value The methodology can be used to simulate dynamical systems characterized by differential equations, in an interactive CAD and optimization providing faster on‐line solutions and speeding up design guidelines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".