Robust design of experiments using constrained stochastic optimization
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
Process models that are affected by uncertainties need a robust mechanism to account for them in the model based design of experiments (DOE). The aim of this study is to design a set of experiments to estimate the parameters of multiscale kinetic models for the catalytic decomposition of ammonia. Along with uncertainties in the model, the problem is challenging due to constraints on experimental conditions. A stochastic D-optimal design is used to find the optimal experimental conditions using maximization of the expectation of properties of the Fisher information matrix (FIM). The expectation of FIM is calculated by sample average approximation (SAA) based on Monte Carlo simulations. Particle swarm optimization (PSO) is used to perform stochastic optimization to find the optimal set of experimental conditions. A novel method based on the rescaling of velocities is proposed for handling of equality and inequality constraints in particle swarm optimization.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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