A scalable neural bundle map for multiphysics prediction in lithium-ion battery across varying configurations
Efficient and accurate prediction of Multiphysics evolution across diverse cell geometries is fundamental to the design, management and safety of lithium-ion batteries. However, existing computational frameworks struggle to capture the coupled electrochemical, thermal, and mechanical dynamics across diverse cell geometries and varying operating conditions. Here, we present a Neural Bundle Map (NBM), a mathematically rigorous framework that reformulates multiphysics evolution as a bundle map over a geometric base manifold. This approach enables the complete decoupling of geometric complexity from underlying physical laws, ensuring strong operator continuity across varying domains. Our fram...
| Authors | Zhiwei Zhao, Changqing Liu, Jie Lin et al. |
|---|---|
| Primary category | cs.CE |
| Published | 17 March 2026 |
| Last updated at source | 17 March 2026 |
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