Functional reconstruction of dynamical systems from time series using genetic programming
Why this work is in the frame
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Bibliographic record
Abstract
Reconstruction of a chaotic system from its measurement is a challenging problem. It requires the determination of an embedding dimension and a nonlinear mapping that approximates the underlying unknown dynamics. We propose the use of genetic programming (GP) to find the exact functional form and embedding dimension of an unknown dynamical system automatically. Using functional operators of addition, multiplication, and time-delay, with the least-squares estimation technique, we use GP to reconstruct the exact chaotic polynomial system and its embedding dimension from a time series. If the underlying dynamic does not come from a polynomial system, the proposed GP method will produce an optimal polynomial predictor for the time series. Simulations showed that the GP approach outperformed a radial basis function neural network in predicting both polynomial and nonpolynomial chaotic systems.
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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 | 0.000 | 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 it