A Simple Explicit Meta-Model for Probabilistic Design of Dynamic Systems with Multiple Mixed Inputs
Why this work is in the frame
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Bibliographic record
Abstract
Most systems have multiple inputs that comprise of a mixture of excitations and component parameters. Excitations are different from component parameters in that they are always functions of time. In mechanical systems, these include applied forces, applied displacements, system settings, systems configurations and operating conditions. It would be convenient to include multiple excitations and multiple component parameters in a meta-model to take advantage of the inherent computation speed needed for timely probability-based design optimization. In the development of the meta-model in this paper, we treat the component parameters in the same manner as the excitations and thus, in both cases, form time-sampled vectors. A design-of-experiments training regime creates a single input matrix, and using the mechanistic model, a single output matrix. Finally, a simple, explicit, meta-model is developed that turns an arbitrary vector of contiguous multiple excitations and multiple component parameters into the corresponding output vector (herein, the response). The approach provides an appealing and efficient solution to the multiple, mixed input problem, and in addition, requires only off-the-shelf computer software. The efficacy of the meta-model is shown through probability-based design optimization (PBDO) of a tire-wheel assembly, modelled as a mass-spring-damper system with nonlinear hysteresis, under a combination of practical inputs.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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