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PhD position - Machine learning based simulation of realistic signals for an enhanced automatic diagnostic in non-destructive testing applications
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Publicerad: 3 dagar sedan
Sista ansökningsdatum: okt 31
Plats: Grenoble, Frankrike
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PhD position - Machine learning based simulation of realistic signals for an enhanced automatic diagnostic in non-destructive testing applications

SL-DRT-19-0657

RESEARCH FIELD

Mathematics - Numerical analysis - Simulation

ABSTRACT

Model based solutions for automatic diagnostic in the field on non-destructive testing are currently a topic of great interest in both academic and industrial communities. Their ultimate objective is to provide a qualitative or quantitative evaluation of the inspected material state (sound, flawed, flawed with anomaly dimensions or criticality) in an industrial context like a production line. Such tools, providing inputs for real-time process control, contribute to the general trend in Europe that aims at modernizing Industry and services [1]. The CEA LIST institute is an internationally recognized research institution in the field of nondestructive testing. It develops the CIVA software [2], which offers multi-physics models and is considered as a leading product for simulation for NDT applications. Accurate models able to reproduce experimental signals prove very helpful in an inversion process aiming at classifying or characterizing flaws [3]. However, as they do not account for disturbances and parameters variability occurring during an experimental acquisition, simulated signals inherently look “perfect” and are, for instance, easily distinguishable from experimental data. This PhD subject aims at improving the match between simulation and experimental data, by augmenting the simulation with another contribution on can generally refer to as “noise”. The strategy proposed to obtain such noise contribution is to apply machine-learning techniques like dictionary learning to a set of representative experimental data. Alternatively, a deep learning model can be trained to analyze real data and then distinguish between contents (flaw signals) and style (the rest, which is not simulated by physical models). Afterwards, the augmented simulation tool will be able to reproduce closely experimental data, take into account specific discrepancies due to a particular environment and reproduce the variability observed experimentally. It will thus enhance the performance of model based tools developed at CEA LIST for sensibility analysis, management of uncertainty and diagnostic. REFERENCES[1] http://ec.europa.eu/research/participants/portal/desktop/en/opportunities/h2020/topics/dt-fof-08-2019.html[2] www.extende.com[3] M. Salucci et al., "Real-Time NDT-NDE Through an Innovative Adaptive Partial Least Squares SVR Inversion Approach," in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 11, pp. 6818-6832, Nov. 2016

LOCATION

Département Imagerie Simulation pour le Contrôle (LIST)

Laboratoire Simulation et Modélisation en Electro-magnétisme

Saclay

CONTACT PERSON

MIORELLI Roberto

CEA

DRT/DISC//LSME

CEA Saclay - Digiteo | Bat. 565-PC120 | F-91191 Gif-sur-Yvette Cedex

Phone number: 0169085057

Email: roberto.miorelli@cea.fr

START DATE

Start date on 01-09-2019

THESIS SUPERVISOR

MASSA Andrea

University of Trento, Centralesupelec

DISI - ELEDIA

ELEDIA Research CenterUniversity of Trento - DISIVia Sommarive 5, Povo 38123, Trento (Italy)

Phone number: +39 0461 282057 / +39 0461 285227

Email: andrea.massa@ing.unitn.it

« The age limit is 26 years for PhD offers and 30 years old for post-doc offers. »

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