A reanalysis is a physically consistent set of optimally merged simulated model states and
historical observational data, using data assimilation. High computational costs for modeled …

We propose InSituNet, a deep learning based surrogate model to support parameter space
exploration for ensemble simulations that are visualized in situ. In situ visualization …

This paper introduces ALPINE, a flyweight in situ infrastructure. The infrastructure is
designed for leading-edge supercomputers, and has support for both distributed-memory …

In this survey, we discuss the challenges of executing scientific workflows as well as existing
Machine Learning (ML) techniques to alleviate those challenges. We provide the context …

Data reduction is increasingly being applied to scientific data for numerical simulations,
scientific visualizations and data analyses. It is most often used to lower I/O and storage …

The SENSEI generic in situ interface is an API that promotes code portability and reusability.
From the simulation view, a developer can instrument their code with the SENSEI API and …

A growing disparity between supercomputer computation speeds and I/O rates makes it
increasingly infeasible for applications to save all results for offline analysis. Instead …

A key trend facing extreme-scale computational science is the widening gap between
computational and I/O rates, and the challenge that follows is how to best gain insight from …

With ever-increasing volumes of scientific data produced by high-performance computing
applications, significantly reducing data size is critical because of limited capacity of storage …

This paper presents an efficient algorithm for the progressive approximation of Wasserstein
barycenters of persistence diagrams, with applications to the visual analysis of ensemble …