AI integration is revolutionizing the landscape of HPC simulations, enhancing the
importance, use, and performance of AI-driven HPC workflows. This paper surveys the …

Serverless computing is gaining popularity for machine learning (ML) serving workload due
to its autonomous resource scaling, easy to use and pay-per-use cost model. Existing …

Powerful detectors at modern experimental facilities routinely collect data at multiple GB/s.
Online analysis methods are needed to enable the collection of only interesting subsets of …

Research process automation–the reliable, efficient, and reproducible execution of linked
sets of actions on scientific instruments, computers, data stores, and other resources–has …

A concise and measurable set of FAIR (Findable, Accessible, Interoperable and Reusable)
principles for scientific data is transforming the state-of-practice for data management and …

This study develops a neural network-based approach for emulating high-resolution
modeled precipitation data with comparable statistical properties but at greatly reduced …

We address the increasing complexity of scientific workflows in the context of high-
performance computing (HPC) and their associated need for robust, adaptable, and flexible …

Scientific applications are increasingly adopting Artificial Intelligence (AI) techniques to
advance science. High-performance computing centers are evaluating emerging novel …

In today's production machine learning (ML) systems, models are continuously trained,
improved, and deployed. ML design and training are becoming a continuous workflow of …

In this paper we demonstrate direct data streaming from instruments and detectors at a large-
scale experimental facility to a supercomputer for real-time data processing and feedback …