scholarly journals Machine Learning and Understanding for Intelligent Extreme Scale Scientific Computing and Discovery. DOE Workshop Report, January 7–9, 2015, Rockville, MD

2015 ◽  
Author(s):  
Michael Berry ◽  
Thomas E. Potok ◽  
Prasanna Balaprakash ◽  
Hank Hoffmann ◽  
Raju Vatsavai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scientific Computing ◽  
Workshop Report ◽  
Extreme Scale

scholarly journals Jupyter: Thinking and Storytelling with Code and Data

2021 ◽  
Author(s):  
Brian Granger ◽  
Fernando Pérez
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Community Of Practice ◽  
Scientific Computing ◽  
Data Science ◽  
Climate Science ◽  
Three Dimensions ◽  
Open Source Project ◽  
Interactive Computing ◽  
The Impact

Project Jupyter is an open-source project for interactive computing widely used in data science, machine learning, and scientific computing. We argue that even though Jupyter helps users perform complex, technical work, Jupyter itself solves problems that are fundamentally human in nature. Namely, Jupyter helps humans to think and tell stories with code and data. We illustrate this by describing three dimensions of Jupyter: interactive computing, computational narratives, and  the idea that Jupyter is more than software. We illustrate the impact of these dimensions on a community of practice in Earth and climate science.

scholarly journals Graph coarsening: from scientific computing to machine learning

SeMA Journal ◽  
2022 ◽  
Author(s):  
Jie Chen ◽  
Yousef Saad ◽  
Zechen Zhang
Keyword(s):  
Machine Learning ◽  
Spectral Properties ◽  
Scientific Computing ◽  
Algebraic Multigrid ◽  
Graph Reduction ◽  
Original Graph ◽  
Common Strategy ◽  
Algebraic Multigrid Methods ◽  
General Method ◽  
Graph Coarsening

AbstractThe general method of graph coarsening or graph reduction has been a remarkably useful and ubiquitous tool in scientific computing and it is now just starting to have a similar impact in machine learning. The goal of this paper is to take a broad look into coarsening techniques that have been successfully deployed in scientific computing and see how similar principles are finding their way in more recent applications related to machine learning. In scientific computing, coarsening plays a central role in algebraic multigrid methods as well as the related class of multilevel incomplete LU factorizations. In machine learning, graph coarsening goes under various names, e.g., graph downsampling or graph reduction. Its goal in most cases is to replace some original graph by one which has fewer nodes, but whose structure and characteristics are similar to those of the original graph. As will be seen, a common strategy in these methods is to rely on spectral properties to define the coarse graph.

scholarly journals Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence

2019 ◽  
Author(s):  
Nathan Baker ◽  
Frank Alexander ◽  
Timo Bremer ◽  
Aric Hagberg ◽  
Yannis Kevrekidis ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Basic Research ◽  
Research Needs ◽  
Workshop Report

scholarly journals Fault Modeling of Extreme Scale Applications Using Machine Learning

2016 ◽  
Author(s):  
Abhinav Vishnu ◽  
Hubertus van Dam ◽  
Nathan R. Tallent ◽  
Darren J. Kerbyson ◽  
Adolfy Hoisie
Keyword(s):  
Machine Learning ◽  
Fault Modeling ◽  
Extreme Scale

scholarly journals Jupyter: Thinking and Storytelling with Code and Data

2021 ◽  
Author(s):  
Brian Granger ◽  
Fernando Pérez
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Community Of Practice ◽  
Scientific Computing ◽  
Data Science ◽  
Climate Science ◽  
Three Dimensions ◽  
Open Source Project ◽  
Interactive Computing ◽  
The Impact

Project Jupyter is an open-source project for interactive computing widely used in data science, machine learning, and scientific computing. We argue that even though Jupyter helps users perform complex, technical work, Jupyter itself solves problems that are fundamentally human in nature. Namely, Jupyter helps humans to think and tell stories with code and data. We illustrate this by describing three dimensions of Jupyter: interactive computing, computational narratives, and  the idea that Jupyter is more than software. We illustrate the impact of these dimensions on a community of practice in Earth and climate science.

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