Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43966078" target="_blank" >RIV/49777513:23520/22:43966078 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.23919/FUSION49751.2022.9841291" target="_blank" >https://doi.org/10.23919/FUSION49751.2022.9841291</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FUSION49751.2022.9841291" target="_blank" >10.23919/FUSION49751.2022.9841291</a>

Result language
angličtina
Original language name
Hybrid Neural Network Augmented Physics-based Models for Nonlinear Filtering
Original language description
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.
Czech name
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Czech description
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Project
—
Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 25th International Conference on Information Fusion, FUSION 2022
ISBN
978-1-73774-972-1
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
1-6
Publisher name
IEEE
Place of publication
Linköping, Sweden
Event location
Linköping, Sweden
Event date
Jul 4, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000855689000065

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