Computer Science > Machine Learning
[Submitted on 13 Apr 2022 (v1), last revised 14 Sep 2022 (this version, v2)]
Title:Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering
View PDFAbstract:In this paper we present a hybrid neural network augmented physics-based modeling (APBM) framework for Bayesian nonlinear latent space estimation. The proposed APBM strategy allows for model adaptation when new operation conditions come into play or the physics-based model is insufficient (or incomplete) to properly describe the latent phenomenon. One advantage of the APBMs and our estimation procedure is the capability of maintaining the physical interpretability of estimated states. Furthermore, we propose a constraint filtering approach to control the neural network contributions to the overall model. We also exploit assumed density filtering techniques and cubature integration rules to present a flexible estimation strategy that can easily deal with nonlinear models and high-dimensional latent spaces. Finally, we demonstrate the efficacy of our methodology by leveraging a target tracking scenario with nonlinear and incomplete measurement and acceleration models, respectively.
Submission history
From: Ahmet Demirkaya [view email][v1] Wed, 13 Apr 2022 15:43:27 UTC (6,581 KB)
[v2] Wed, 14 Sep 2022 19:04:43 UTC (6,582 KB)
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