https://doi.org/10.1140/epje/s10189-023-00267-w
Regular Article - Flowing Matter
Toward learning Lattice Boltzmann collision operators
1
Eindhoven University of Technology, 5600, Eindhoven, MB, The Netherlands
2
Los Alamos National Laboratory, 87545, Los Alamos, NM, USA
3
Consiglio Nazionale della Ricerche-IAC, Rome, Italy
Received:
13
December
2022
Accepted:
12
February
2023
Published online:
6
March
2023
In this work, we explore the possibility of learning from data collision operators for the Lattice Boltzmann Method using a deep learning approach. We compare a hierarchy of designs of the neural network (NN) collision operator and evaluate the performance of the resulting LBM method in reproducing time dynamics of several canonical flows. In the current study, as a first attempt to address the learning problem, the data were generated by a single relaxation time BGK operator. We demonstrate that vanilla NN architecture has very limited accuracy. On the other hand, by embedding physical properties, such as conservation laws and symmetries, it is possible to dramatically increase the accuracy by several orders of magnitude and correctly reproduce the short and long time dynamics of standard fluid flows.
© The Author(s) 2023
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