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This work presents an efficient online system identification approach by integrating a reduced sigma points–based filter with a high-fidelity mechanics-based state-space hysteretic finite-element modeling framework. The efficacy and computational efficiency of any sampling/sigma points–based nonlinear filtering process are conditional on the number of sigma/sample points required by the filter at each time step to quantify statistical properties of the involved quantities, as well as on the accuracy and computational cost of the underlying system model. A scaled spherical simplex filter (S3F) with a significantly decreased sigma points set size is thus presented that is able to achieve similar robustness and accuracy as the state-of-the-art sigma points unscented Kalman filter (UKF) for an -dimensional state-space, yet with approximately 50% less computational requirements. The filtering framework is …