[2604.02520] Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteries

Physics > Data Analysis, Statistics and Probability arXiv:2604.02520 (physics) [Submitted on 2 Apr 2026] Title:Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteries Authors:Malik Hassanaly, Corey R. Randall, Peter J. Weddle, Paul J. Gasper, Conlain Kelly, Tanvir R. Tanim, Kandler Smith View a PDF of the paper titled Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteries, by Malik Hassanaly and 5 other authors View PDF HTML (experimental) Abstract:Diagnosing the internal state of Li-ion batteries is critical for battery research, operation of real-world systems, and prognostic evaluation of remaining lifetime. By using physics-based models to perform probabilistic parameter estimation via Bayesian calibration, diagnostics can account for the uncertainty due to model fitness, data noise, and the observability of any given parameter. However, Bayesian calibration in Li-ion batteries using electrochemical data is computationally intensive even when using a fast surrogate in place of physics-based models, requiring many thousands of model evaluations. A fully amortized alternative is neural posterior estimation (NPE). NPE shifts the computational burden from the parameter estimation step to data generation and model training, reducing the parameter estimation time from minutes to milliseconds, enabling real-time applications. The present work shows that NPE calibrates parameters equal...