Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Artificial intelligence (AI) based downlink channel state information (CSI) prediction for
frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems …

Deep Neural Networks (DNNs) have had a significant impact on domains like autonomous
vehicles and smart cities through low-latency inferencing on edge computing devices close …

Data assimilation can reduce the model-form errors of RANS simulations. A spatially
distributed corrective parameter field can be introduced into the closure model, whose …

Stochastic gradient descent (SGD) algorithms, with constant momentum and its variants
such as Adam, are the optimization methods of choice for training deep neural networks …

Abstract The Reynolds-averaged Navier–Stokes (RANS) equations provide a
computationally efficient method for solving fluid flow problems in engineering applications …

While fairness-aware machine learning algorithms have been receiving increasing attention,
the focus has been on centralized machine learning, leaving decentralized methods …

We consider gradient descent withmomentum', a widely used method for loss function
minimization in machine learning. This method is often used withNesterov acceleration' …

Diffusion models (DMs) are a powerful generative framework that have attracted significant
attention in recent years. However, the high computational cost of training DMs limits their …

Large, carefully partitioned datasets are essential to train neural networks and standardize
performance benchmarks. As a result, we have set up new repositories to make our electron …