We present the Exascale Framework for High Fidelity coupled Simulations (EFFIS), a
workflow and code coupling framework developed as part of the Whole Device Modeling …

The applications being developed within the US Exascale Computing Project (ECP) to run
on imminent Exascale computers will generate scientific results with unprecedented fidelity …

In an in-situ workflow, multiple components such as simulation and analysis applications are
coupled with streaming data transfers. The multiplicity of possible configurations …

Rapid growth in data, computational methods, and computing power is driving a remarkable
revolution in what variously is termed machine learning (ML), statistical learning …

Rapid growth in scientific data and a widening gap between computational speed and I/O
bandwidth make it increasingly infeasible to store and share all data produced by scientific …

Efficient data compression is becoming increasingly critical for storing scientific data
because many scientific applications produce vast amounts of data. This paper presents an …

Science applications preparing for the exascale era are increasingly exploring in situ
computations comprising of simulation‐analysis‐reduction pipelines coupled in‐memory …

The ever-increasing volumes of scientific data combined with sophisticated techniques for
extracting information from them have led to the increasing popularity of ensemble …

We present the Feature Tracking Kit (FTK), a framework that simplifies, scales, and delivers
various feature-tracking algorithms for scientific data. The key of FTK is our simplicial …

Modern computational science and engineering applications are being improved by
advances in scientific machine learning. Data-driven methods such as dynamic mode …