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2. Multi-fidelity modeling with different input domain definitions using Deep Gaussian Processes

Author

Nouredine Melab, El-Ghazali Talbi, Ali Hebbal, Mathieu Balesdent, Loïc Brevault, DTIS, ONERA, Université Paris Saclay [Palaiseau], ONERA-Université Paris-Saclay, Optimisation de grande taille et calcul large échelle (BONUS), Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), and The work of Ali Hebbal is a funded by ONERA - The French Aerospace Lab and the University of Lille through a PhD thesis. This work is also part of two projects (HERACLES and MUFIN) funded by ONERA. The Experiments presented in this paper were carried out using the Grid’5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr).

Subjects
FOS: Computer and information sciences, Domain of a function, Computer Science - Machine Learning, Control and Optimization, Computer science, media_common.quotation_subject, 0211 other engineering and technologies, Fidelity, Machine Learning (stat.ML), 02 engineering and technology, Machine Learning (cs.LG), Domain (software engineering), symbols.namesake, 0203 mechanical engineering, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Statistics - Machine Learning, multifidélité, modélisation bayésienne, Gaussian process, 021106 design practice & management, media_common, business.industry, Quantum Physics, Computer Graphics and Computer-Aided Design, Computer Science Applications, 020303 mechanical engineering & transports, Test case, Control and Systems Engineering, espace entree dimension variée, symbols, Global Positioning System, Engineering design process, business, Parametrization, Algorithm, Software, processus gaussien
Abstract

International audience; Multi-fidelity approaches combine different models built on a scarce but accurate data-set (high-fidelity data-set), and a large but approximate one (low-fidelity data-set) in order to improve the prediction accuracy. Gaussian Processes (GPs) are one of the popular approaches to exhibit the correlations between these different fidelity levels. Deep Gaussian Processes (DGPs) that are functional compositions of GPs have also been adapted to multi-fidelity using the Multi-Fidelity Deep Gaussian process model (MF-DGP). This model increases the expressive power compared to GPs by considering non-linear correlations between fidelities within a Bayesian framework. However, these multi-fidelity methods consider only the case where the inputs of the different fidelity models are defined over the same domain of definition (e.g., same variables, same dimensions). However, due to simplification in the modeling of the low-fidelity, some variables may be omitted or a different parametrization may be used compared to the high-fidelity model. In this paper, Deep Gaussian Processes for multi-fidelity (MF-DGP) are extended to the case where a different parametrization is used for each fidelity. The performance of the proposed multifidelity modeling technique is assessed on analytical test cases and on structural and aerodynamic real physical problems.

Published
2020

3. Dislocation detection in field environments: A belief functions contribution

Author

Carl T. Haas, Saiedeh Razavi, Emmanuel Duflos, Philippe Vanheeghe, Department of Civil and Environmental Engineering [Waterloo], University of Waterloo [Waterloo], Sequential Learning (SEQUEL), Laboratoire d'Informatique Fondamentale de Lille (LIFL), Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria), LAGIS-SI, Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS), The research work was sponsored by a CNRS International Scientific Collaboration Program (PICS)., This paper results from the collaboration between the Laboratoire d'Automatique Génie Informatique et Signal (UMR CNRS 8219, Lille, France) and the Department of Civil and Environmental Engineering of the University of Waterloo (Canada)., Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Université de Lille, Sciences et Technologies-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lille, Sciences Humaines et Sociales-Centre National de la Recherche Scientifique (CNRS)-Inria Lille - Nord Europe, Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS), and Université de Lille, Sciences et Technologies-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)-Centrale Lille-Centre National de la Recherche Scientifique (CNRS)

Subjects
GPS, 0211 other engineering and technologies, Construction materials, 02 engineering and technology, Machine learning, computer.software_genre, Field (computer science), Dislocation detection, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Artificial Intelligence, Position (vector), 021105 building & construction, 0202 electrical engineering, electronic engineering, information engineering, Radio-frequency identification, Mathematics, RFID, business.industry, Frame (networking), General Engineering, Function (mathematics), belief functions, Sensors network, Computer Science Applications, Discrete time and continuous time, Global Positioning System, 020201 artificial intelligence & image processing, Artificial intelligence, business, Focus (optics), Algorithm, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

Highlights? The greedy acceptance criterion for the glowworms updating positions is proposed. ? The new formulas for the glowworms movement are proposed. ? Uniform design experiments were investigated the effect of parameters. ? The proposed improvement algorithms were effective than the classical algorithm. Dislocation is defined as the change between discrete sequential locations of critical items in field environments such as large construction projects. Dislocations on large sites of materials and critical items for which discrete time position estimates are available represent critical state changes. The ability to detect dislocations automatically for tens of thousands of items can ultimately improve project performance significantly. Detecting these dislocations in a noisy information environment where low cost radio frequency identification tags are attached to each piece of material, and the material is moved sometimes only a few meters, is the main focus of this study. We propose in this paper a method developed in the frame of belief functions to detect dislocations. The belief function framework is well-suited for such a problem where both uncertainty and imprecision are inherent to the problem. We also show how to deal with the calculations. This method has been implemented in a controlled experimental setting. The results of these experiments show the ability of the proposed method to detect materials dislocation over the site reliably. Broader application of this approach to both animate and inanimate objects is possible.

Published
2012