用于增强PIC/FLIP解决方案的侵入式去卷积神经网络
Intrusive deconvolutional neural networks for enhancing PIC/FLIP solutions
摘要：
传统的流体流动预测需要大量的计算资源。尽管最近在并行和GPU计算方面取得了进展，但实时运行流体流动预测的能力通常是不可行的。最近开发的机器学习方法在高保真数据上进行训练，在训练集之外的表现不令人满意，并且在训练之后消除了利用遗留代码的能力。我们提出了一种新的方法，该方法使用深度学习方法，可以在低保真度流体流动求解器中使用，以显着提高低保真度模拟的准确性。由此产生的求解器可以实现精确的同时减少计算时间高达100倍。深度神经网络是在低保真和高保真数据的组合上训练的，并且所得到的求解器被称为多保真求解器。所提出的方法是通过增强流体流动模拟器，被称为PIC/FLIP，这是一个流行的流体流动模拟器在计算机生成的图像领域。
英文摘要：
Traditional fluid flow predictions require large computational resources. Despite recent progress in parallel and GPU computing, the ability to run fluid flow predictions in real-time is often infeasible. Recently developed machine learning approaches, which are trained on high-fidelity data, perform unsatisfactorily outside the training set and remove the ability of utilising legacy codes after training. We propose a novel methodology that uses a deep learning approach that can be used within a low-fidelity fluid flow solver to significantly increase the accuracy of the low-fidelity simulations. The resulting solver enables accurate while reducing computational times up to 100 times. The deep neural network is trained on a combination of low- and high-fidelity data, and the resulting solver is referred to as a multi-fidelity solver. The proposed methodology is demonstrated by means of enhancing a fluid flow simulator, known as PIC/FLIP, which is a popular fluid flow simulator in the field of computer generated imagery.
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