Conformalized Transfer Learning

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A transfer learning framework for SOH forecasting that integrates domain adaptation via MMD and uncertainty quantification via Conformal Prediction.

Why It Matters

It is the core methodological novelty, combining uncertainty quantification (CP) with domain adaptation (MMD) in a transfer learning setup for SOH forecasting.

Evidence

uncertainty-aware transfer learning framework is proposed, combining a Long Short-Term Memory (LSTM) model with domain adaptation via Maximum Mean Discrepancy (MMD) and uncertainty quantification through Conformal Prediction (CP)

Related Papers

• openalex-2603.24475-conformalized-transfer-learning-for-li-ion-battery-state-of