Towards learning Lattice Boltzmann collision operators
arXiv:2212.06124 · doi:10.1140/epje/s10189-023-00267-w
Abstract
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 was 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.
References in corpus (19)
- Turbulence Modeling in the Age of Data
- Super-resolution reconstruction of turbulent flows with machine learning
- A Physics Informed Machine Learning Approach for Reconstructing Reynolds Stress Modeling Discrepancies Based on DNS Data
- Generalized Lattice Boltzmann Method with multi-range pseudo-potential
- Efficient kinetic method for fluid simulation beyond the Navier-Stokes equation
- Deep Fluids: A Generative Network for Parameterized Fluid Simulations
- Lattice-Boltzmann Method for Non-Newtonian Fluid Flows
- Comprehensive comparison of collision models in the lattice Boltzmann framework: Theoretical investigations
- Lattice Boltzmann Methods for thermal flows: continuum limit and applications to compressible Rayleigh-Taylor systems
- Controlling Rayleigh-Bénard convection via Reinforcement Learning
- Lattice ellipsoidal statistical BGK model for thermal non-equilibrium flows
- Towards Exascale Lattice Boltzmann computing
- Relativistic Lattice Boltzmann Methods: Theory and Applications
- Deep learning velocity signals allows to quantify turbulence intensity
- Lettuce: PyTorch-based Lattice Boltzmann Framework
- Corner transport upwind lattice Boltzmann model for bubble cavitation
- Half-range lattice Boltzmann models for the simulation of Couette flow using the Shakhov collision term
- Lattice Boltzmann method for semiclassical fluids
- A Numerical Proof of Shell Model Turbulence Closure