HMSPC: A Hybrid Mechanistic-Stochastic Physical-Continuous Model for Battery Dynamics
Why this work is in the frame
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Bibliographic record
Abstract
Battery voltage dynamics are irregularly sampled, noise-corrupted, and strongly regime-dependent, properties that challenge standard sequential models. I propose HMSPC (Hybrid Mechanistic-Stochastic Physical-Continuous Model), a continuous-time latent variable model that addresses these challenges through two key components: a gated input-conditioned latent ODE that explicitly incorporates exogenous observations (current and temperature) into continuous-time state evolution, and a heteroscedastic observation model with uncertainty regularization that produces calibrated predictive variance. Built on the Latent ODE (Rubanova et al., 2019) framework, HMSPC replaces purely autonomous latent dynamics with a learned gating mechanism that adaptively controls how strongly operating conditions influence trajectory evolution at each integration step. Evaluated on the MIT-Stanford dataset (Severson et al., 2019) against Latent ODE and Vanilla Neural ODE (Chen et al., 2018) baselines across 5 seeds, HMSPC achieves a mean RMSE of 32.37 ± 1.34 mV compared to 58.33 ± 4.19 mV for Latent ODE, yielding a 45% reduction, alongside well-calibrated uncertainty estimates (ECE 0.078). Ablation studies confirm that input-conditioned drift and heteroscedastic noise each contribute meaningfully to both accuracy and calibration.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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