Data-Augmented Numerical Integration in State Prediction: Rule Selection
Abstract
This paper deals with the state prediction of nonlinear stochastic dynamic systems. The emphasis is laid on a solution to the integral Chapman-Kolmogorov equation by a deterministic-integration-rule-based point-mass method. A novel concept of reliable data-augmented, i.e., mathematics- and data-informed, integration rule is developed to enhance the point-mass state predictor, where the trained neural network (representing data contribution) is used for the selection of the best integration rule from a set of available rules (representing mathematics contribution). The proposed approach combining the best properties of the standard mathematics-informed and novel data-informed rules is thoroughly discussed.
Keywords
Cite
@article{arxiv.2412.06376,
title = {Data-Augmented Numerical Integration in State Prediction: Rule Selection},
author = {Jindrich Dunik and Ladislav Kral and Jakub Matousek and Ondrej Straka and Marek Brandner},
journal= {arXiv preprint arXiv:2412.06376},
year = {2025}
}
Comments
This work has been accepted to IFAC for publication - IFAC SYSID24