相关论文: HIGAN: Cosmic Neutral Hydrogen with Generative Adv…

We carried out synthetic observations of interstellar atomic hydrogen at 21cm wavelength by utilizing the magneto-hydrodynamical numerical simulations of the inhomogeneous turbulent interstellar medium (ISM) Inoue and Inutsuka (2012). The…

We propose Hamiltonian Quantum Generative Adversarial Networks (HQuGANs), to learn to generate unknown input quantum states using two competing quantum optimal controls. The game-theoretic framework of the algorithm is inspired by the…

Generative Adversarial Networks (GANs) have shown great promise in modeling high dimensional data. The learning objective of GANs usually minimizes some measure discrepancy, \textit{e.g.}, $f$-divergence~($f$-GANs) or Integral Probability…

In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a \textit {mode collapse} issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are…

As a major interstellar medium, the atomic neutral hydrogen (HI) plays an important role in the galaxy evolution. It provides the ingredient for star formation, and sensitively traces the internal processes and external perturbations…

We investigate the possible presence of neutral hydrogen (HI) in intergalactic filaments at very low redshift ($z\sim 0.08$), by stacking a set of 274,712 2dFGRS galaxy pairs over 21-cm maps obtained with dedicated observations conducted…

The extension of image generation to video generation turns out to be a very difficult task, since the temporal dimension of videos introduces an extra challenge during the generation process. Besides, due to the limitation of memory and…

We propose a temporally coherent generative model addressing the super-resolution problem for fluid flows. Our work represents a first approach to synthesize four-dimensional physics fields with neural networks. Based on a conditional…

The spatial distribution of neutral hydrogen (HI) in the Universe contains a wealth of cosmological information. The 21 cm emission line can be used to map the HI up to very high redshift and therefore reveal us something about the…

We introduce an innovative approach employing Cycle Generative Adversarial Networks (Cycle-GANs) to accurately simulate Carbon Monoxide (CO) emissions by learning features identified in thermal dust emission maps from the Planck satellite…

Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications. This task can be formulated as a combinatorial problem, and it takes many hours…

Efforts are underway to measure the global 21 cm signal from neutral hydrogen, which is a powerful probe of the early universe, using NASA radio telescopes on the far side of the Moon. Physics-based models of the signal are computationally…

We use a large N-body simulation to examine the detectability of HI in emission at redshift z ~ 1, and the constraints imposed by current observations on the neutral hydrogen mass function of galaxies at this epoch. We consider three…

I review the potential for observing cosmic reionization using the HI 21cm line of neutral hydrogren. Studies include observations of the evolution of large scale structure of the IGM (density, excitation temperature, and neutral fraction),…

Atmospheric retrievals on exoplanets usually involve computationally intensive Bayesian sampling methods. Large parameter spaces and increasingly complex atmospheric models create a computational bottleneck forcing a trade-off between…

Connecting numerical simulations to observations is essential to understanding the physics of galactic winds. Our Galaxy hosts a large-scale, multi-phase nuclear wind, whose dense gas has been detected using HI and molecular line…

We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of…

Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning. However, to…

Generating images via the generative adversarial network (GAN) has attracted much attention recently. However, most of the existing GAN-based methods can only produce low-resolution images of limited quality. Directly generating…

Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as "downscaling" in the atmospheric sciences: improving the spatial resolution of low-resolution…