TIDE: Trustworthy and Interpretable Battery Degradation Estimation with Contextual Learning and Symbolic Distillation

arXiv:2607.14640 · cs.LG · Submitted 2026-07-16 · Read on arXiv

cs.LG

Submitted: 2026-07-16

Updated: 2026-09-09

Comments: 8 pages, 11 figures, WI-IAT 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 35× in this paper · explained in 1 episode
• Distillation — 16× in this paper · explained in 2 episodes
• Interpretability — 15× in this paper · explained in 15 episodes
• Fidelity — 10× in this paper · explained in 2 episodes
• Consistency — 9× in this paper · explained in 3 episodes
• Hybrid Models — 3× in this paper · explained in 2 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Robustness — 2× in this paper · explained in 5 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Core — 1× in this paper · explained in 2 episodes
• Feature Engineering — 1× in this paper · explained in 3 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Knowledge Distillation — 1× in this paper · explained in 11 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Monotonic Violation Rate — 1× in this paper
• Multimodal Inputs — 1× in this paper · explained in 2 episodes
• Root Mean Squared Error — 1× in this paper
• Singapore University of Social Sciences — 1× in this paper
• Symbolic Regression — 1× in this paper · explained in 5 episodes
• Verification — 1× in this paper · explained in 2 episodes

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