scholarly journals Emulation of Cosmological Mass Maps with Conditional Generative Adversarial Networks

2021 ◽  
Vol 4 ◽  
Author(s):  
Nathanaël Perraudin ◽  
Sandro Marcon ◽  
Aurelien Lucchi ◽  
Tomasz Kacprzak
Keyword(s):  
Gravitational Lensing ◽  
Cosmological Parameters ◽  
Similarity Index ◽  
Power Spectra ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Summary Statistics ◽  
Redshift Distribution ◽  
Adversarial Networks ◽  
The Universe

Weak gravitational lensing mass maps play a crucial role in understanding the evolution of structures in the Universe and our ability to constrain cosmological models. The prediction of these mass maps is based on expensive N-body simulations, which can create a computational bottleneck for cosmological analyses. Simulation-based emulators of map summary statistics, such as the matter power spectrum and its covariance, are starting to play increasingly important role, as the analytical predictions are expected to reach their precision limits for upcoming experiments. Creating an emulator of the cosmological mass maps themselves, rather than their summary statistics, is a more challenging task. Modern deep generative models, such as Generative Adversarial Networks (GAN), have demonstrated their potential to achieve this goal. Most existing GAN approaches produce simulations for a fixed value of the cosmological parameters, which limits their practical applicability. We propose a novel conditional GAN model that is able to generate mass maps for any pair of matter density Ωm and matter clustering strength σ8, parameters which have the largest impact on the evolution of structures in the Universe, for a given source galaxy redshift distribution n(z). Our results show that our conditional GAN can interpolate efficiently within the space of simulated cosmologies, and generate maps anywhere inside this space with good visual quality high statistical accuracy. We perform an extensive quantitative comparison of the N-body and GAN -generated maps using a range of metrics: the pixel histograms, peak counts, power spectra, bispectra, Minkowski functionals, correlation matrices of the power spectra, the Multi-Scale Structural Similarity Index (MS-SSIM) and our equivalent of the Fréchet Inception Distance. We find a very good agreement on these metrics, with typical differences are <5% at the center of the simulation grid, and slightly worse for cosmologies at the grid edges. The agreement for the bispectrum is slightly worse, on the <20% level. This contribution is a step toward building emulators of mass maps directly, capturing both the cosmological signal and its variability. We make the code1 and the data2 publicly available.

scholarly journals Cosmology with weak lensing surveys

2005 ◽  
Vol 363 (1837) ◽  
pp. 2675-2691 ◽  
Author(s):  
Dipak Munshi ◽  
Patrick Valageas
Keyword(s):  
Dark Matter ◽  
Dark Energy ◽  
Gravitational Lensing ◽  
Large Scale ◽  
High Redshift ◽  
Cosmological Parameters ◽  
Power Spectra ◽  
Weak Lensing ◽  
Satellite Mission ◽  
The Universe

Weak gravitational lensing is responsible for the shearing and magnification of the images of high-redshift sources due to the presence of intervening mass. Since the lensing effects arise from deflections of the light rays due to fluctuations of the gravitational potential, they can be directly related to the underlying density field of the large-scale structures. Weak gravitational surveys are complementary to both galaxy surveys and cosmic microwave background observations as they probe unbiased nonlinear matter power spectra at medium redshift. Ongoing CMBR experiments such as WMAP and a future Planck satellite mission will measure the standard cosmological parameters with unprecedented accuracy. The focus of attention will then shift to understanding the nature of dark matter and vacuum energy: several recent studies suggest that lensing is the best method for constraining the dark energy equation of state. During the next 5 year period, ongoing and future weak lensing surveys such as the Joint Dark Energy Mission (JDEM; e.g. SNAP) or the Large-aperture Synoptic Survey Telescope will play a major role in advancing our understanding of the universe in this direction. In this review article, we describe various aspects of probing the matter power spectrum and the bispectrum and other related statistics with weak lensing surveys. This can be used to probe the background dynamics of the universe as well as the nature of dark matter and dark energy.

Generative Adversarial Networks (GANs)

2021 ◽  
Vol 54 (3) ◽  
pp. 1-42
Author(s):  
Divya Saxena ◽  
Jiannong Cao
Keyword(s):  
Optimization Technique ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Network Architectures ◽  
Research Directions ◽  
Research Issues ◽  
Design And Optimization ◽  
Adversarial Networks ◽  
Comprehensive Survey ◽  
Selection Of

Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, and data. However, there exist major challenges in training of GANs, i.e., mode collapse, non-convergence, and instability, due to inappropriate design of network architectre, use of objective function, and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions, and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on the broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present promising research directions in this rapidly growing field.

scholarly journals Conditional Wasserstein Generative Adversarial Networks for Fast Detector Simulation

2021 ◽  
Vol 251 ◽  
pp. 03055
Author(s):  
John Blue ◽  
Braden Kronheim ◽  
Michelle Kuchera ◽  
Raghuram Ramanujan
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Detector Response ◽  
Event Simulation ◽  
Simulation Process ◽  
Adversarial Networks ◽  
Wide Range ◽  
Detector Simulation

Detector simulation in high energy physics experiments is a key yet computationally expensive step in the event simulation process. There has been much recent interest in using deep generative models as a faster alternative to the full Monte Carlo simulation process in situations in which the utmost accuracy is not necessary. In this work we investigate the use of conditional Wasserstein Generative Adversarial Networks to simulate both hadronization and the detector response to jets. Our model takes the 4-momenta of jets formed from partons post-showering and pre-hadronization as inputs and predicts the 4-momenta of the corresponding reconstructed jet. Our model is trained on fully simulated tt events using the publicly available GEANT-based simulation of the CMS Collaboration. We demonstrate that the model produces accurate conditional reconstructed jet transverse momentum (pT) distributions over a wide range of pT for the input parton jet. Our model takes only a fraction of the time necessary for conventional detector simulation methods, running on a CPU in less than a millisecond per event.

scholarly journals Generative Adversarial Network for Image Super-Resolution Combining Texture Loss

2020 ◽  
Vol 10 (5) ◽  
pp. 1729 ◽  
Author(s):  
Yuning Jiang ◽  
Jinhua Li
Keyword(s):  
Loss Function ◽  
Network Structure ◽  
Similarity Index ◽  
Super Resolution ◽  
Experimental Results ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Visual Evaluation ◽  
Low Frequencies ◽  
Adversarial Networks

Objective: Super-resolution reconstruction is an increasingly important area in computer vision. To alleviate the problems that super-resolution reconstruction models based on generative adversarial networks are difficult to train and contain artifacts in reconstruction results, we propose a novel and improved algorithm. Methods: This paper presented TSRGAN (Super-Resolution Generative Adversarial Networks Combining Texture Loss) model which was also based on generative adversarial networks. We redefined the generator network and discriminator network. Firstly, on the network structure, residual dense blocks without excess batch normalization layers were used to form generator network. Visual Geometry Group (VGG)19 network was adopted as the basic framework of discriminator network. Secondly, in the loss function, the weighting of the four loss functions of texture loss, perceptual loss, adversarial loss and content loss was used as the objective function of generator. Texture loss was proposed to encourage local information matching. Perceptual loss was enhanced by employing the features before activation layer to calculate. Adversarial loss was optimized based on WGAN-GP (Wasserstein GAN with Gradient Penalty) theory. Content loss was used to ensure the accuracy of low-frequency information. During the optimization process, the target image information was reconstructed from different angles of high and low frequencies. Results: The experimental results showed that our method made the average Peak Signal to Noise Ratio of reconstructed images reach 27.99 dB and the average Structural Similarity Index reach 0.778 without losing too much speed, which was superior to other comparison algorithms in objective evaluation index. What is more, TSRGAN significantly improved subjective visual evaluations such as brightness information and texture details. We found that it could generate images with more realistic textures and more accurate brightness, which were more in line with human visual evaluation. Conclusions: Our improvements to the network structure could reduce the model’s calculation amount and stabilize the training direction. In addition, the loss function we present for generator could provide stronger supervision for restoring realistic textures and achieving brightness consistency. Experimental results prove the effectiveness and superiority of TSRGAN algorithm.

scholarly journals GRAVITATIONAL LENSING BOUND ON THE AVERAGE REDSHIFT OF GAMMA RAY BURSTS IN MODELS WITH EVOLVING LENSES

1999 ◽  
Vol 08 (04) ◽  
pp. 507-517 ◽  
Author(s):  
DEEPAK JAIN ◽  
N. PANCHAPAKESAN ◽  
S. MAHAJAN ◽  
V. B. BHATIA
Keyword(s):  
Gravitational Lensing ◽  
Gamma Ray ◽  
Cosmological Parameters ◽  
Evolutionary Model ◽  
Gamma Ray Bursts ◽  
The Other ◽  
Upper Limit ◽  
Evolving Model ◽  
Evolving Models ◽  
The Universe

Identification of gravitationally lensed Gamma Ray Bursts (GRBs) in the BATSE 4B catalog can be used to constrain the average redshift <z> of the GRBs. In this paper we investigate the effect of evolving lenses on the <z> of GRBs in different cosmological models of the universe. The cosmological parameters Ω and Λ have an effect on the <z> of GRBs. The other factor which can change the <z> is the evolution of galaxies. We consider three evolutionary model of galaxies. In particular, we find that the upper limit on <z> of GRBs is higher in evolving model of galaxies as compared to non-evolving models of galaxies.

scholarly journals InvNet: Encoding Geometric and Statistical Invariances in Deep Generative Models

2020 ◽  
Vol 34 (04) ◽  
pp. 4377-4384
Author(s):  
Ameya Joshi ◽  
Minsu Cho ◽  
Viraj Shah ◽  
Balaji Pokuri ◽  
Soumik Sarkar ◽  
...  
Keyword(s):  
Image Data ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Complex Data ◽  
Challenging Problem ◽  
Two Phase ◽  
Adversarial Networks ◽  
Generative Modeling ◽  
Lack Of Control ◽  
Real World Problems

Generative Adversarial Networks (GANs), while widely successful in modeling complex data distributions, have not yet been sufficiently leveraged in scientific computing and design. Reasons for this include the lack of flexibility of GANs to represent discrete-valued image data, as well as the lack of control over physical properties of generated samples. We propose a new conditional generative modeling approach (InvNet) that efficiently enables modeling discrete-valued images, while allowing control over their parameterized geometric and statistical properties. We evaluate our approach on several synthetic and real world problems: navigating manifolds of geometric shapes with desired sizes; generation of binary two-phase materials; and the (challenging) problem of generating multi-orientation polycrystalline microstructures.

scholarly journals Learning Generative Adversarial Networks from Multiple Data Sources

2019 ◽  
Author(s):  
Trung Le ◽  
Quan Hoang ◽  
Hung Vu ◽  
Tu Dinh Nguyen ◽  
Hung Bui ◽  
...  
Keyword(s):  
Generative Models ◽  
Data Sources ◽  
Black Hair ◽  
Primary Data ◽  
Generative Adversarial Networks ◽  
Auxiliary Data ◽  
Adversarial Networks ◽  
Multiple Data ◽  
Data Source ◽  
Push And Pull

Generative Adversarial Networks (GANs) are a powerful class of deep generative models. In this paper, we extend GAN to the problem of generating data that are not only close to a primary data source but also required to be different from auxiliary data sources. For this problem, we enrich both GANs' formulations and applications by introducing pushing forces that thrust generated samples away from given auxiliary data sources. We term our method Push-and-Pull GAN (P2GAN). We conduct extensive experiments to demonstrate the merit of P2GAN in two applications: generating data with constraints and addressing the mode collapsing problem. We use CIFAR-10, STL-10, and ImageNet datasets and compute Fréchet Inception Distance to evaluate P2GAN's effectiveness in addressing the mode collapsing problem. The results show that P2GAN outperforms the state-of-the-art baselines. For the problem of generating data with constraints, we show that P2GAN can successfully avoid generating specific features such as black hair.

scholarly journals Deep image synthesis from intuitive user input: A review and perspectives

2021 ◽  
Vol 8 (1) ◽  
pp. 3-31
Author(s):  
Yuan Xue ◽  
Yuan-Chen Guo ◽  
Han Zhang ◽  
Tao Xu ◽  
Song-Hai Zhang ◽  
...  
Keyword(s):  
Image Synthesis ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Art And Design ◽  
User Input ◽  
Adversarial Networks ◽  
Benchmark Datasets ◽  
Deep Image ◽  
Realistic Images

AbstractIn many applications of computer graphics, art, and design, it is desirable for a user to provide intuitive non-image input, such as text, sketch, stroke, graph, or layout, and have a computer system automatically generate photo-realistic images according to that input. While classically, works that allow such automatic image content generation have followed a framework of image retrieval and composition, recent advances in deep generative models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and flow-based methods have enabled more powerful and versatile image generation approaches. This paper reviews recent works for image synthesis given intuitive user input, covering advances in input versatility, image generation methodology, benchmark datasets, and evaluation metrics. This motivates new perspectives on input representation and interactivity, cross fertilization between major image generation paradigms, and evaluation and comparison of generation methods.

scholarly journals CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis

2021 ◽  
Author(s):  
Amin Heyrani Nobari ◽  
Muhammad Fathy Rashad ◽  
Faez Ahmed
Keyword(s):  
Novelty Detection ◽  
Computational Design ◽  
Generative Models ◽  
Machine Learning Techniques ◽  
Generative Adversarial Networks ◽  
Creative Design ◽  
Design Synthesis ◽  
Adversarial Networks ◽  
Automated Method ◽  
Learning Techniques

Abstract Modern machine learning techniques, such as deep neural networks, are transforming many disciplines ranging from image recognition to language understanding, by uncovering patterns in big data and making accurate predictions. They have also shown promising results for synthesizing new designs, which is crucial for creating products and enabling innovation. Generative models, including generative adversarial networks (GANs), have proven to be effective for design synthesis with applications ranging from product design to metamaterial design. These automated computational design methods can support human designers, who typically create designs by a time-consuming process of iteratively exploring ideas using experience and heuristics. However, there are still challenges remaining in automatically synthesizing ‘creative’ designs. GAN models, however, are not capable of generating unique designs, a key to innovation and a major gap in AI-based design automation applications. This paper proposes an automated method, named CreativeGAN, for generating novel designs. It does so by identifying components that make a design unique and modifying a GAN model such that it becomes more likely to generate designs with identified unique components. The method combines state-of-art novelty detection, segmentation, novelty localization, rewriting, and generative models for creative design synthesis. Using a dataset of bicycle designs, we demonstrate that the method can create new bicycle designs with unique frames and handles, and generalize rare novelties to a broad set of designs. Our automated method requires no human intervention and demonstrates a way to rethink creative design synthesis and exploration. For details and code used in this paper please refer to http://decode.mit.edu/projects/creativegan/.

scholarly journals Generative adversarial networks for generating synthetic features for Wi-Fi signal quality

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0260308
Author(s):  
Mauro Castelli ◽  
Luca Manzoni ◽  
Tatiane Espindola ◽  
Aleš Popovič ◽  
Andrea De Lorenzo
Keyword(s):  
Synthetic Data ◽  
Real Data ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Signal Quality ◽  
Quality Service ◽  
High Quality ◽  
The Real ◽  
Adversarial Networks ◽  
High Quality Service

Wireless networks are among the fundamental technologies used to connect people. Considering the constant advancements in the field, telecommunication operators must guarantee a high-quality service to keep their customer portfolio. To ensure this high-quality service, it is common to establish partnerships with specialized technology companies that deliver software services in order to monitor the networks and identify faults and respective solutions. A common barrier faced by these specialized companies is the lack of data to develop and test their products. This paper investigates the use of generative adversarial networks (GANs), which are state-of-the-art generative models, for generating synthetic telecommunication data related to Wi-Fi signal quality. We developed, trained, and compared two of the most used GAN architectures: the Vanilla GAN and the Wasserstein GAN (WGAN). Both models presented satisfactory results and were able to generate synthetic data similar to the real ones. In particular, the distribution of the synthetic data overlaps the distribution of the real data for all of the considered features. Moreover, the considered generative models can reproduce the same associations observed for the synthetic features. We chose the WGAN as the final model, but both models are suitable for addressing the problem at hand.

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