Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering
This work addresses model adaptation and interpretability in nonlinear filtering for applications like target tracking, representing an incremental improvement by combining neural networks with physics-based models.
The paper tackles the problem of Bayesian nonlinear latent space estimation by introducing a hybrid neural network augmented physics-based modeling framework, which maintains physical interpretability and adapts to new conditions, demonstrating efficacy in a target tracking scenario with nonlinear and incomplete models.
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.