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2d-navier-stokes-solver

As the field of Computational Fluid Dynamics (CFD) progresses, the fluid flows are more and more analysed by using simulations with the help of high speed computers. In order to solve and analyse these fluid flows we require intensive simulation involving mathematical equations which governs the fluid flow, these are Navier Stokes (NS) equation. Solving these equations has become a necessity as almost every problem which is related to fluid flow analysis call for solving of Navier Stokes equation. These NS equations are partial differential equations so different numerical methods are used to solve these equations. Solving these partial differential equations so different numerical methods requires large amount of computing power and huge amount of memory is in play. Only practical feasible way to solve these equation is write a parallel program to solve them, which can then be run on powerful hardware capable of parallel processing to get the desired results High speed supercomputer will provide us very good performance in terms of reduction in execution time. In paper focus will be on finite volume as a numerical method. We will also see what GPGPU (General-Purpose computing on Graphics Processing Units) is and how we are taking its advantages to solve CFD problems.

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cfd-gcn

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code for performing active flow control of the 2D Karman street using Deep Reinforcement Learning

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A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

deep-flow-prediction

A framework for fluid flow (Reynolds-averaged Navier Stokes) predictions with deep learning

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deeplearning.ai , By Andrew Ng, All slide and notebook + code and some material.

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Deep learning library for solving differential equations and more

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Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)

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