2025/01/01 by Kumar Anurag, Kumar Ankur Anurag, Kasra Azizi +6 · 1 citation
Computer Science · Physics and Astronomy · #Neural Networks and Reservoir Computing #Model Reduction and Neural Networks #Gaussian Processes and Bayesian Inference
paper · pdf · doi:10.1016/j.ifacol.2025.12.251
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’s prediction step to generate state estimates in high-dimensional or chaotic regimes where nominal mathematical models may fail. Meanwhile, the UKF’s measurement update integrates real-time sensor data to correct potential drift in the data-driven model. We demonstrate RCUKF’s effectiveness on well-known benchmark problems and a real-time vehicle trajectory estimation task in a high-fidelity simulation environment.