Comparative Analysis of Machine Learning Regression Models for Unknown Dynamics
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
System identification methods can enable scientists and engineers to model a system for analysis, estimation, or activity planning. Machine Learning (ML) regression algorithms are a useful data-driven system identification tool that can be used in many fields, such as signal identification, wireless communication, or dynamic modelling. However, researchers must select an appropriate algorithm depending on the system’s complexity. In this study, we evaluate the performance of three ML algorithms: Polynomial Fit, Artificial Neural Network (ANN), and Sparse Identification of Non-linear Dynamics (SINDy), to perform model identification in four different time-invariant dynamic environments. We trained each algorithm using 100 simulated data sets and validated them with ten different trajectories. We compare the results using an error distribution framework, demonstrating that ANN had the lowest prediction error, SINDy had comparable performance for three dynamic environments, but none of the algorithms reliably predicted the discontinuous accelerations. This study demonstrated that spacecraft control systems with continuous dynamics may benefit from ML methods.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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