论文标题
用生成对抗网络编码大规模宇宙学结构
Encoding large scale cosmological structure with Generative Adversarial Networks
论文作者
论文摘要
最近，已经提出了一种称为生成对抗网络（GAN）的神经网络，作为快速生成类似模拟数据集的解决方案，以试图绕过大量计算和昂贵的宇宙学模拟以在时间和计算能力方面运行。在目前的工作中，我们建立和训练了一个gan，以进一步研究这种方法的优势和局限性。然后，我们提出了一种新颖的方法，其中我们利用训练有素的gan来构建简单的自动编码器（AE），作为构建预测模型的第一步。 GAN和AE均经过从两种类型的N体模拟（即2D和3D模拟）发行的图像进行培训。我们发现，GAN成功地生成了与训练的图像一致的新图像。然后，我们证明AE设法从模拟图像中有效提取信息，令人满意地推断了GAN的潜在编码以生成具有相似大型结构的图像。
Recently a type of neural networks called Generative Adversarial Networks (GANs) has been proposed as a solution for fast generation of simulation-like datasets, in an attempt to bypass heavy computations and expensive cosmological simulations to run in terms of time and computing power. In the present work, we build and train a GAN to look further into the strengths and limitations of such an approach. We then propose a novel method in which we make use of a trained GAN to construct a simple autoencoder (AE) as a first step towards building a predictive model. Both the GAN and AE are trained on images issued from two types of N-body simulations, namely 2D and 3D simulations. We find that the GAN successfully generates new images that are statistically consistent with the images it was trained on. We then show that the AE manages to efficiently extract information from simulation images, satisfyingly inferring the latent encoding of the GAN to generate an image with similar large scale structures.
