The popularity of machine learning (ML), deep learning (DL) and artificial intelligence (AI)
has risen sharply in recent years. Despite this spike in popularity, the inner workings of ML …

In recent years, the limits of the multicore approach emerged in the so-called “dark silicon”
issue and diminishing returns of an ever-increasing core count. Hardware manufacturers …

In the context of science, the well-known adage" a picture is worth a thousand words" might
well be" a model is worth a thousand datasets." In this manuscript we introduce the SciML …

Spectral Indices derived from multispectral remote sensing products are extensively used to
monitor Earth system dynamics (eg vegetation dynamics, water bodies, fire regimes). The …

Including prior knowledge is important for effective machine learning models in physics and
is usually achieved by explicitly adding loss terms or constraints on model architectures …

The difficulty of deploying various deep learning (DL) models on diverse DL hardware has
boosted the research and development of DL compilers in the community. Several DL …

Scientific computing is increasingly incorporating the advancements in machine learning
and the ability to work with large amounts of data. At the same time, machine learning …

Abstract We introduce $\texttt {Yao} $, an extensible, efficient open-source framework for
quantum algorithm design. $\texttt {Yao} $ features generic and differentiable programming …

JAX and PyTorch are two popular Python autodifferentiation frameworks. JAX is based
around pure functions and functional programming. PyTorch has popularised the use of an …

Buildings are one of the main drivers of global energy consumption and CO 2 emissions.
Efficient energy management systems will have to integrate renewable energy sources with …