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Authors: Ankit Chiplunkar 1 ; Emmanuel Rachelson 2 ; Michele Colombo 3 and Joseph Morlier 4

Affiliations: 1 Airbus Operations S.A.S., Université de Toulouse, CNRS and ISAE-SUPAERO, France ; 2 Universite de Toulouse, ISAE and DISC, France ; 3 Airbus Operations S.A.S., France ; 4 Université de Toulouse, CNRS and ISAE-SUPAERO, France

ISBN: 978-989-758-173-1

Keyword(s): Gaussian Process, Kernel Methods, Variational Inference, Multi-output Regression, Flight-test data.

Related Ontology Subjects/Areas/Topics: Applications ; Gaussian Processes ; Pattern Recognition ; Sensors and Early Vision ; Software Engineering ; Theory and Methods

Abstract: In this paper a sparse approximation of inference for multi-output Gaussian Process models based on a Variational Inference approach is presented. In Gaussian Processes a multi-output kernel is a covariance function over correlated outputs. Using a general framework for constructing auto- and cross-covariance functions that are consistent with the physical laws, physical relationships among several outputs can be imposed. One major issue with Gaussian Processes is efficient inference, when scaling up-to large datasets. The issue of scaling becomes even more important when dealing with multiple outputs, since the cost of inference increases rapidly with the number of outputs. In this paper we combine the use of variational inference for efficient inference with multi-output kernels enforcing relationships between outputs. Results of the proposed methodology for synthetic data and real world applications are presented. The main contribution of this paper is the application and validatio n of our methodology on a dataset of real aircraft flight tests, while imposing knowledge of aircraft physics into the model. (More)

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Paper citation in several formats:
Chiplunkar, A.; Rachelson, E.; Colombo, M. and Morlier, J. (2016). Sparse Physics-based Gaussian Process for Multi-output Regression using Variational Inference.In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-173-1, pages 437-445. DOI: 10.5220/0005700504370445

@conference{icpram16,
author={Ankit Chiplunkar. and Emmanuel Rachelson. and Michele Colombo. and Joseph Morlier.},
title={Sparse Physics-based Gaussian Process for Multi-output Regression using Variational Inference},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2016},
pages={437-445},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005700504370445},
isbn={978-989-758-173-1},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Sparse Physics-based Gaussian Process for Multi-output Regression using Variational Inference
SN - 978-989-758-173-1
AU - Chiplunkar, A.
AU - Rachelson, E.
AU - Colombo, M.
AU - Morlier, J.
PY - 2016
SP - 437
EP - 445
DO - 10.5220/0005700504370445

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