📄 中文摘要
准确、实时且无损地估计锂离子电池内部状态对于预测退化、优化使用策略和延长操作寿命至关重要。PINEAPPLE（用于锂离子电池电极预后参数推断的物理信息神经进化算法）是一种新颖的框架，结合了物理信息神经网络（PINNs）与进化搜索算法，实现了快速、可扩展且可解释的参数推断，具有应用于下一代电池的潜力。该元学习的PINN利用基本物理原理，实现了电极行为的准确零样本预测，测试误差低于0.1%。
PINEAPPLE：用于锂离子电池电极预后参数推断的物理信息神经进化算法
📄 English Summary
PINEAPPLE: Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter Inference in Lithium-Ion Battery Electrodes
Accurate, real-time, and non-destructive estimation of internal states in lithium-ion batteries is crucial for predicting degradation, optimizing usage strategies, and extending operational lifespan. PINEAPPLE (Physics-Informed Neuro-Evolution Algorithm for Prognostic Parameter inference in Lithium-ion battery Electrodes) is a novel framework that integrates physics-informed neural networks (PINNs) with an evolutionary search algorithm, enabling rapid, scalable, and interpretable parameter inference with potential applications in next-generation batteries. The meta-learned PINN leverages fundamental physics principles to achieve accurate zero-shot predictions of electrode behavior, with test errors below 0.1%.