Deep generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAs) have been demonstrated to produce images of high visual quality. However, the existing hardware severely limits the size of the images that can be generated. The rapid growth of high dimensional data in many fields of science therefore poses a significant challenge for generative models. In cosmology, the large-scale, three-dimensional matter distribution, modeled with N-body simulations, plays a crucial role in understanding the evolution of the universe. As these simulations are computationally very expensive, GANs have recently generated interest as a possible method to emulate these datasets, but they have been, so far, mostly limited to two dimensional data. In this work, we introduce a new benchmark for the generation of three dimensional N-body simulations, in order to stimulate new ideas in the machine learning community and move closer to the practical use of generative models in cosmology. As a first benchmark result, we propose a scalable GAN approach for training a generator of N-body three-dimensional cubes. Our technique relies on two key building blocks, (i) splitting the generation of the high-dimensional data into smaller parts, and (ii) using a multi-scale approach that efficiently captures global image features that might otherwise be lost in the splitting process. We evaluate the performance of our model for the generation of N-body samples using various statistical measures commonly used in cosmology. Our results show that the proposed model produces samples of high visual quality, although the statistical analysis reveals that capturing rare features in the data poses significant problems for the generative models. We make the data, quality evaluation routines, and the proposed GAN architecture publicly available at https://github.com/nperraud/3DcosmoGAN
Encoding large scale cosmological structure with Generative Adversarial Networks
Université Paris-Saclay（巴黎萨克雷大学）；CNRS（法国国家科学研究中心）；Institut d’Astrophysique Spatiale（空间天体物理研究所）；INRIA Saclay（法国国家信息与自动化研究所萨克雷分部）；Laboratoire de recherche en informatique（计算机科学实验室）；Universidad Complutense（马德里康普顿斯大学）
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling
University of Oxford（牛津大学）；Aalto University（阿尔托大学）；Institute of Science and Technology Austria（奥地利科学技术研究所）；University of Copenhagen（哥本哈根大学）；Imperial College London（伦敦帝国学院）
Generative Bayesian Computation as a Scalable Alternative to Gaussian Process Surrogates
Booth School of Business, University of Chicago（芝加哥大学商学院）；Department of Systems Engineering and Operations Research, George Mason University（乔治·马歇尔大学系统工程与运营管理系）
GLASS: Global Latent Aggregation with Slot-based Set Decoding for Scalable All-Atom Crystal Generation
Institute for Artificial Intelligence（人工智能研究所）；University of Stuttgart（斯图加特大学）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；NEC Labs Europe（NEC欧洲实验室）
Scalable Generative Sampling and Multilevel Estimation for Lattice Field Theories Near Criticality
Berlin Institute for the Foundations of Learning and Data (BIFOLD)（柏林学习与数据基础研究所）；Technische Universität Berlin（柏林工业大学）；Higgs Centre for Theoretical Physics, School of Physics and Astronomy, The University of Edinburgh（爱丁堡大学物理与天文学学院希格斯理论物理中心）；Deutsches Elektronen-Synchrotron (DESY)（德国电子同步加速器）；RIKEN Center for Advanced Intelligence Project (AIP)（理化学研究院先进智能项目中心）
Scalable physics-informed deep generative model for solving forward and inverse stochastic differential equations
Institute of Interdisciplinary Research for Mathematics and Applied Science, School of Mathematics and Statistics, Huazhong University of Science and Technology（华中科技大学数学与统计学院交叉学科数学与应用科学研究所）；Department of Mathematics, Shanghai Normal University（上海师范大学数学系）
