相关论文: HIGAN: Cosmic Neutral Hydrogen with Generative Adv…
The coastal regions of the eastern and southern United States are impacted by severe storm events, leading to significant loss of life and properties. Accurately forecasting storm surge and wind impacts from hurricanes is essential for…
By means of zoom-in hydrodynamic simulations we quantify the amount of neutral hydrogen (HI) hosted by groups and clusters of galaxies. Our simulations, which are based on an improved formulation of smoothed particle hydrodynamics (SPH),…
Generative models and in particular Generative Adversarial Networks (GANs) have become very popular and powerful data generation tool. In recent years, major progress has been made in extending this concept into the quantum realm. However,…
In this paper, we study a physics-informed algorithm for Wasserstein Generative Adversarial Networks (WGANs) for uncertainty quantification in solutions of partial differential equations. By using groupsort activation functions in…
Standard Distributional Synthetic Controls (DSC) estimate counterfactual distributions by minimizing the Euclidean $L_2$ distance between quantile functions. We demonstrate that this geometric reliance renders estimators fragile: they lack…
Event simulation for electron neutrino interactions plays a foundational role in precision measurements in particle physics experiments, yet the computational demand of traditional Monte Carlo methods remains a significant challenge,…
Generative adversarial networks (GANs) have enjoyed much success in learning high-dimensional distributions. Learning objectives approximately minimize an $f$-divergence ($f$-GANs) or an integral probability metric (Wasserstein GANs)…
Generative Adversarial Networks (GANs) are one of the most practical methods for learning data distributions. A popular GAN formulation is based on the use of Wasserstein distance as a metric between probability distributions.…
Hydrogen, the most abundant element in the Universe, has traditionally been used to investigate astrophysical processes within and around our own Galaxy. In its chemically neutral, atomic form (known as HI in the astronomical literature),…
The characterisation of the atomic and molecular hydrogen content of high-redshift galaxies is a major observational challenge that will be addressed over the coming years with a new generation of radio telescopes. We investigate this…
Stochastic generators are useful for estimating climate impacts on various sectors. Projecting climate risk in various sectors, e.g. energy systems, requires generators that are accurate (statistical resemblance to ground-truth), reliable…
Neutral atomic hydrogen (HI) observations are fundamental to understand the dynamics of galaxies, their assembly, the fuelling of their star formation and environmental interactions. HI studies have so far been limited by the capabilities…
This paper reports on WaterGAN, a generative adversarial network (GAN) for generating realistic underwater images from in-air image and depth pairings in an unsupervised pipeline used for color correction of monocular underwater images.…
The presence, distribution and kinematics of atomic neutral hydrogen in the central regions of galaxies can be traced by the HI 21~cm line observed in absorption. Depending only on the strength of the radio continuum, the associated…
Intensity mapping surveys will provide access to a coarse view of the cosmic large-scale structure in unprecedented large volumes at high redshifts. Given the large fractions of the sky that can be efficiently scanned using emission from…
In this work we test Wasserstein distance in conjunction with persistent homology, as a tool for discriminating large scale structures of simulated universes with different values of $\sigma_8$ cosmological parameter (present…
\textsc{Hi} intensity mapping will provide maps of the large-scale distribution of neutral hydrogen (\textsc{Hi}) in the universe. These are prime candidates to be used to constrain primordial non-Gaussianity using the Large Scale Structure…
The evolution of neutral hydrogen (HI) across redshifts is a powerful probe of cosmology, large scale structure in the universe and the intergalactic medium. Using a data-driven halo model to describe the distribution of HI in the…
Deep Neural Networks (DNNs) come into the limelight in High Energy Physics (HEP) in order to manipulate the increasing amount of data encountered in the next generation of accelerators. Recently, the HEP community has suggested Generative…
When researchers develop new econometric methods it is common practice to compare the performance of the new methods to those of existing methods in Monte Carlo studies. The credibility of such Monte Carlo studies is often limited because…