Lat-Net: Compressing Lattice Boltzmann Flow Simulations using Deep Neural Networks

Computational Fluid Dynamics (CFD) is a hugely important subject with applications in almost every engineering field, however, fluid simulations are extremely computationally and memory demanding. Towards this end, we present Lat-Net, a method for compressing both the computation time and memory usa...

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Bibliographic Details
Published inarXiv.org
Main Author Hennigh, Oliver
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 25.05.2017
Subjects
Compressing
Computational fluid dynamics
Computer memory
Computer simulation
Electromagnetism
Geometric accuracy
Neural networks
Simulation
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Summary:Computational Fluid Dynamics (CFD) is a hugely important subject with applications in almost every engineering field, however, fluid simulations are extremely computationally and memory demanding. Towards this end, we present Lat-Net, a method for compressing both the computation time and memory usage of Lattice Boltzmann flow simulations using deep neural networks. Lat-Net employs convolutional autoencoders and residual connections in a fully differentiable scheme to compress the state size of a simulation and learn the dynamics on this compressed form. The result is a computationally and memory efficient neural network that can be iterated and queried to reproduce a fluid simulation. We show that once Lat-Net is trained, it can generalize to large grid sizes and complex geometries while maintaining accuracy. We also show that Lat-Net is a general method for compressing other Lattice Boltzmann based simulations such as Electromagnetism.
ISSN:2331-8422