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Job Record #15543
TitleIntelligent data collection for efficient model surrogating
LocationFrance, TOULOUSE
InternationalYes, international applications are welcome
Closure Date* None *
Context and Objective

Recent advances in Artificial Intelligence like machine learning based on deep neural networks 
allows the learning of any function of interest, given enough input/output samples. While the 
offline learning time can be long, the online computation of the output given the inputs takes 
constant time, generally very short. This paves a way for using such techniques for building 
surrogate models/functions where long computations are mandatory at high frequency. This is 
particularly true in elaborate physics models where iterative computation is necessary in every 
cell of a discretized space, for instance to solve local partial derivative equations as ones 
encountered in fluid motion, heat exchange among others.
Using surrogate models based on deep learning has already shown interesting results like in 
[TSSP16], but questions are still opened in order for such methods to spread out. While the 
learning phase of the neural network is well known, the problem of collecting the data using 
the original function before learning (or the loop including both of them) is much less 
investigated. In practice, it may have dramatic consequences for several reasons:
• Deep learning generally needs a lot of data as it does not take into account any modelling 
hypothesis. If the original function is expensive or slow to compute (for instance very precise 
simulator in fluid dynamics, or real environment), the global amount of data should be 
• There is a trade-off between the quantity of data used for learning, and the quality of the 
surrogate model obtained, but it is difficult to assess since the various deep architecture 
properties are not completly modelled.
• The impact of the quality of the data used, particularly in terms of variety, is still not clearly 
understood: the data should cover all the cases, and certainly need to be more dense around 
sensitive places. Yet, methods to identify which data should be generate to complete the 
dataset are still missing.
Such problems have been studied as hyperparmeter optimization (parameters for data 
collection can be seen as hyperparameters), for instance with the pioneering ParamILS 
algorithm [HHLBS09]. Techniques have evolved, for instance, by adding a model of the error 
of the surro- gate model like in the AutoML [FH18] framework, and/or by using the sequential 
nature of the hyperparameter search, like in the SMAC framework [HHLB11]. All these 
approaches consider the sequential problem of selecting good (hyper-)parameters, seeing the 
results in terms of error, and then selecting another set of good (hyper-)parameters and so 
on. Another approach is the reinforcement learning framework, which has shown very 
impressive results in the few last years [MKS+13, MGM+18, Ope18]. Such techniques can use 
deep learning techniques in order to build a model of the expected future errors after 
choosing some hyper-parameters.
This internship will investigate the first few steps towards algorithms and methodologies for 
intelligent data collection taking into account the criteria above, using different techniques 
going from statistical modelling to reinforcement learning. This will be applied to building 
surrogate models of the Poisson equation resolution used in fluid mechanics.
Applicant profile: Candidate should have high motivation for new challenges and innovative 
approaches, a background in machine learning and/or its mathematical foundations, C++ and 
python development as well as linux environment. Knowledge in fluid mechanics and more 
generally physics simulations would be most appreciated.
Contact Information:
Please mention the CFD Jobs Database, record #15543 when responding to this ad.
Email ApplicationYes
Record Data:
Last Modified13:01:25, Thursday, December 20, 2018

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