Lab Home | Phone | Search | ||||||||
|
||||||||
In this talk we discuss the possibility of learning collision operators for the Lattice Boltzmann Method (LBM) using a deep learning approach. We present results in which a neural network is successfully trained as a surrogate of the single relaxation time BGK operator. We compare the accuracy achieved in the simulation of a few selected benchmarks, employing several approaches for the architecture of the neural network. We show that only by embedding in the neural network physics properties, such as conservation laws and symmetries, it is possible to correctly reproduce the time dynamic of simple fluid flows. Moreover, we present a framework which allows coupling LBM with Direct simulation Monte Carlo (DSMC), paving the way for learning more complex collisional operators, aiming at providing a reliable and computationally efficient description of flows in rarefied conditions. Host: Vitaliy Gyrya (T-5) and Daniel Livescu (CCS-2) |