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Job Record #15544
TitleAccelerating Lattice Boltzmann Methods using deep learning
LocationFrance, TOULOUSE
InternationalYes, international applications are welcome
Closure Date* None *
Context and Objectives

Artificial Intelligence (AI) recently emerges in the engineering fields as a new approach to 
handle complex systems and elaborate physical models. Deep learning is one of those 
methods, based on a training/validation technique, which has shown outstanding results. For 
instance, a Go virtual player (one of the most difficult problem in AI) has been recently trained 
using a deep learning strategy, and has won for the first time a world-class professional in a 
five-game match in 2016.
In fluid mechanics, breakthrough in numerical methods can be expected by using such a 
technique to develop complex physical models or enhance current numerical solvers [1]. This 
project will focus on the Lattice Boltzmann Method (LBM) which was revealed as an effective 
solver to compute low-Mach number flows because of its high-accuracy and low-cost 
advection scheme. Compared with Navier-Stokes solvers, the equations to be solved in LBM 
are discretized in time, space, and velocity, the latter requiring a specific model known as 
lattice where a few discrete velocities are chosen among the continuous velocity space. Such 
a method yields effective computation with outstanding accuracy at low Mach numbers. 
However, the accuracy and numerical costs of the LBM for higher Mach numbers (M > 0.3) is 
still a challenge, which requires new developments.
Therefore, this project intends to improve current LBM methods using a deep learning 
strategy. This internship will focus on the classical weakly compressible 2D formulation 
available in the code Palabos, where the velocity space is discretized with a standard lattice 
2DQ9, i.e. where 9 discrete velocities are used. Note that the more velocities are computed, 
the more expansive and more accurate the simulation is. The main question addressed in this 
internship is: can we compute less velocities while keeping the same level of accuracy? One 
key idea is to use deep learning to learn how to compensate the reduced number of velocities, 
for example through learnt source terms or learnt extra discrete velocities.
This internship for LBM on weakly compressible flows will be a first step towards the im- 
provement of LBM methods at high Mach number flows, where a reduced number of velocities 
at constant accuracy might lead to significant breakthrough.
Contact Information:
Please mention the CFD Jobs Database, record #15544 when responding to this ad.
Email ApplicationYes
Record Data:
Last Modified13:03:54, Thursday, December 20, 2018

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