Neural posterior estimation for scalable and accurate inverse parameter inference in Li-ion batteries
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 co...
| Authors | Malik Hassanaly, Corey R. Randall, Peter J. Weddle et al. |
|---|---|
| Primary category | physics.data-an |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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