AI 中文总结
研究锂离子电池电极尺度放电行为预测难题，提出基于Swin3D Transformer的深度学习替代管道，集成高斯位置编码和时间编码模块，实验表明其预测准确且大幅降低计算开销，为电池设计优化提供高效框架。
AI 中文摘要
基于物理的模拟对于理解锂离子电池的电极尺度放电行为至关重要，但计算成本过高。为解决此问题，我们引入了一种基于Swin3D Transformer的新型深度学习替代管道，直接从体积数据预测时空放电动态。我们的方法集成了两项关键创新：高斯位置编码（GPE），通过适应电极微观结构的复杂几何形状增强空间特征表示；以及一个专门的时间编码模块来捕获非线性时间序列演变。在电化学模拟（ES）数据集上的实验验证表明，我们的管道在预测准确性上显著优于现有最先进的点云基线。此外，该方法将计算开销降低了几个数量级，为高通量电池设计和优化提供了一个可扩展且高效的框架。
英文摘要
Physics-based simulations are essential for understanding the electrode-scale discharge behavior of lithium-ion batteries (LIBs) but suffer from prohibitive computational costs. To address this, we introduce a novel deep learning surrogate pipeline based on the Swin3D Transformer to predict spatiotemporal discharge dynamics directly from volumetric data. Our approach integrates two key innovations: Gaussian Positional Encoding (GPE), which enhances spatial feature representation by adapting to the complex geometry of electrode microstructures, and a specialized Temporal Encoding module to capture non-linear timeseries evolution. Experimental validation on an Electrochemical Simulation (ES) dataset demonstrates that our pipeline significantly outperforms state-of-the-art point cloud baselines in prediction accuracy. Furthermore, the proposed method reduces the computational overhead by orders of magnitude, providing a scalable and efficient framework for high-throughput battery design and optimization.
Journal refLecture Notes in Computer Science, 2027, Lecture Notes in Artificial Intelligence, 16615, pp.120-131
