We look into Generative Adversarial Network (GAN), its prevalent variants and applications
in a number of sectors. GANs combine two neural networks that compete against one …

The application of machine learning in solar physics has the potential to greatly enhance our
understanding of the complex processes that take place in the atmosphere of the Sun. By …

One of the key objectives in geophysics is to characterize the subsurface through the
process of analyzing and interpreting geophysical field data that are typically acquired at the …

The emergence of deep learning model GAN (Generative Adversarial Networks) is an
important turning point in generative modeling. GAN is more powerful in feature and …

Data-driven science and technology offer transformative tools and methods to science. This
review article highlights the latest development and progress in the interdisciplinary field of …

The physical complexity and the large number of degrees of freedom that can be resolved
today by direct numerical simulations of turbulent flows, and by the most sophisticated …

Merging Black Holes in the Low-mass and High-mass Gaps from 2 + 2 Quadruple Systems -
IOPscience This site uses cookies. By continuing to use this site you agree to our use of cookies …

We developed an operational solar flare prediction model using deep neural networks,
named Deep Flare Net (DeFN). DeFN can issue probabilistic forecasts of solar flares in two …

In this study, we make a global total electron content (TEC) forecasting using a novel deep
learning method, which is based on conditional generative adversarial networks. For …

Context. Nanoflares are impulsive energy releases that occur due to magnetic reconnection
in the braided coronal magnetic field, which is a potential mechanism for heating the corona …