{"ID":2884972,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.04985","arxiv_id":"2508.04985","title":"RCUKF: Data-Driven Modeling Meets Bayesian Estimation","abstract":"Accurate modeling is crucial in many engineering and scientific applications, yet obtaining a reliable process model for complex systems is often challenging. To address this challenge, we propose a novel framework, reservoir computing with unscented Kalman filtering (RCUKF), which integrates data-driven modeling via reservoir computing (RC) with Bayesian estimation through the unscented Kalman filter (UKF). The RC component learns the nonlinear system dynamics directly from data, serving as a surrogate process model in the UKF prediction step to generate state estimates in high-dimensional or chaotic regimes where nominal mathematical models may fail. Meanwhile, the UKF measurement update integrates real-time sensor data to correct potential drift in the data-driven model. We demonstrate RCUKF effectiveness on well-known benchmark problems and a real-time vehicle trajectory estimation task in a high-fidelity simulation environment.","short_abstract":"Accurate modeling is crucial in many engineering and scientific applications, yet obtaining a reliable process model for complex systems is often challenging. To address this challenge, we propose a novel framework, reservoir computing with unscented Kalman filtering (RCUKF), which integrates data-driven modeling via r...","url_abs":"https://arxiv.org/abs/2508.04985","url_pdf":"https://arxiv.org/pdf/2508.04985v1","authors":"[\"Kumar Anurag\",\"Kasra Azizi\",\"Francesco Sorrentino\",\"Wenbin Wan\"]","published":"2025-08-07T02:41:43Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"eess.SY\",\"stat.ML\"]","methods":"[]","has_code":false}