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

ArXi:2604.02520v1 Announce Type: cross 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.