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Science-Based Computational Modeling: New Questions New Challenges and Big Data Problems.
Abstract not provided.
Big Science Through Computing At Sandia National Laboratories.
Abstract not provided.
Coupling of Electrochemistry with Surface Science and Computational Modeling to Understand Nanoparticle Electrocatalysts
For presentation at the 244th ECS Meeting, Gothenburg, Sweden, October 8-12, 2023.
Research in progress and other activities of the Institute for Computer Applications in Science and Engineering
This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics and computer science during the period April 1, 1993 through September 30, 1993. The major categories of the current ICASE research program are: (1) applied and numerical mathematics, including numerical analysis and algorithm development; (2) theoretical and computational research in fluid mechanics in selected areas of interest to LaRC, including acoustic and combustion; (3) experimental research in transition and turbulence and aerodynamics involving LaRC facilities and scientists; and (4) computer science.
Telescience workstation
As part of the Telescience Testbed Pilot Program, the Universities Space Research Association/ Research Institute for Advanced Computer Science (USRA/RIACS) proposed to support remote communication by providing a network of human/machine interfaces, computer resources, and experimental equipment which allows: remote science, collaboration, technical exchange, and multimedia communication. The telescience workstation is intended to provide a local computing environment for telescience. The purpose of the program are as follows: (1) to provide a suitable environment to integrate existing and new software for a telescience workstation; (2) to provide a suitable environment to develop new software in support of telescience activities; (3) to provide an interoperable environment so that a wide variety of workstations may be used in the telescience program; (4) to provide a supportive infrastructure and a common software base; and (5) to advance, apply, and evaluate the telescience technolgy base. A prototype telescience computing environment designed to bring practicing scientists in domains other than their computer science into a modern style of doing their computing was created and deployed. This environment, the Telescience Windowing Environment, Phase 1 (TeleWEn-1), met some, but not all of the goals stated above. The TeleWEn-1 provided a window-based workstation environment and a set of tools for text editing, document preparation, electronic mail, multimedia mail, raster manipulation, and system management.
Applied Information Systems Research Program (AISRP). Workshop 2: Meeting Proceedings
The Earth and space science participants were able to see where the current research can be applied in their disciplines and computer science participants could see potential areas for future application of computer and information systems research. The Earth and Space Science research proposals for the High Performance Computing and Communications (HPCC) program were under evaluation. Therefore, this effort was not discussed at the AISRP Workshop. OSSA's other high priority area in computer science is scientific visualization, with the entire second day of the workshop devoted to it.
An Introduction to the E3SM Special Collection: Goals, Science Drivers, Development, and Analysis
Not Available
Educational NASA Computational and Scientific Studies (enCOMPASS)
Educational NASA Computational and Scientific Studies (enCOMPASS) is an educational project of NASA Goddard Space Flight Center aimed at bridging the gap between computational objectives and needs of NASA's scientific research, missions, and projects, and academia's latest advances in applied mathematics and computer science. enCOMPASS achieves this goal via bidirectional collaboration and communication between NASA and academia. Using developed NASA Computational Case Studies in university computer science/engineering and applied mathematics classes is a way of addressing NASA's goals of contributing to the Science, Technology, Education, and Math (STEM) National Objective. The enCOMPASS Web site at http://encompass.gsfc.nasa.gov provides additional information. There are currently nine enCOMPASS case studies developed in areas of earth sciences, planetary sciences, and astrophysics. Some of these case studies have been published in AIP and IEEE's Computing in Science and Engineering magazines. A few university professors have used enCOMPASS case studies in their computational classes and contributed their findings to NASA scientists. In these case studies, after introducing the science area, the specific problem, and related NASA missions, students are first asked to solve a known problem using NASA data and past approaches used and often published in a scientific/research paper. Then, after learning about the NASA application and related computational tools and approaches for solving the proposed problem, students are given a harder problem as a challenge for them to research and develop solutions for. This project provides a model for NASA scientists and engineers on one side, and university students, faculty, and researchers in computer science and applied mathematics on the other side, to learn from each other's areas of work, computational needs and solutions, and the latest advances in research and development. This innovation takes NASA science and engineering applications to computer science and applied mathematics university classes, and makes NASA objectives part of the university curricula. There is great potential for growth and return on investment of this program to the point where every major university in the U.S. would use at least one of these case studies in one of their computational courses, and where every NASA scientist and engineer facing a computational challenge (without having resources or expertise to solve it) would use enCOMPASS to formulate the problem as a case study, provide it to a university, and get back their solutions and ideas.
1999 NCCS Highlights
The NASA Center for Computational Sciences (NCCS) is a high-performance scientific computing facility operated, maintained and managed by the Earth and Space Data Computing Division (ESDCD) of NASA Goddard Space Flight Center's (GSFC) Earth Sciences Directorate. The mission of the NCCS is to advance leading-edge science by providing the best people, computers, and data storage systems to NASA's Earth and space sciences programs and those of other U.S. Government agencies, universities, and private institutions. Among the many computationally demanding Earth science research efforts supported by the NCCS in Fiscal Year 1999 (FY99) are the NASA Seasonal-to-Interannual Prediction Project, the NASA Search and Rescue Mission, Earth gravitational model development efforts, the National Weather Service's North American Observing System program, Data Assimilation Office studies, a NASA-sponsored project at the Center for Ocean-Land-Atmosphere Studies, a NASA-sponsored microgravity project conducted by researchers at the City University of New York and the University of Pennsylvania, the completion of a satellite-derived global climate data set, simulations of a new geodynamo model, and studies of Earth's torque. This document presents highlights of these research efforts and an overview of the NCCS, its facilities, and its people.
Machine Learning for NASA Advanced Information Systems
NASA's Advanced Information Systems Technology (AIST) Program is one of several Technology programs managed by the Earth Science Technology Office (ESTO) in the Earth Science Division (ESD). The AIST Program focuses on advanced information systems and novel computer science technologies that will be needed by NASA Earth Science in the next 5 to 10 years. The three main thrusts of the AIST Program deal with Novel Observing Strategies (NOS), Analytic Collaborative Frameworks (ACF) and Earth System Digital Twins (ESDT). For all these thrusts, Machine Learning (ML) is increasingly being used in multiple aspects of Earth science systems, e.g., for onboard autonomy and decision making, for the analysis of massive and diverse datasets as well as more recently for developing surrogate models that will represent one of the main components of future Digital Twins of the Earth. Particularly, ESDT technologies developed by the AIST Program will allow to develop integrated Earth Science frameworks that will mirror the Earth with state-of-the-art models (Earth system models and others), timely and relevant observations, and analytic tools. These information systems will be used for supporting near- and long-term science and policy decisions. ESDT frameworks will build on previously developed AIST capabilities and technologies to integrate interconnected models with continuous streams of observations, data analytics, data assimilation, simulations, advanced visualizations and the ability to conduct "what-if" scenarios. This talk will describe the three thrusts of the AIST Program with a special focus on Machine Learning and how it is being used at all steps of the Earth Science data lifecycle.
Activities of the Institute for Computer Applications in Science and Engineering
Research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, numerical analysis, and computer science during the period April 1, 1985 through October 2, 1985 is summarized.
Learning a general model of single phase flow in complex 3D porous media
Modeling effective transport properties of 3D porous media, such as permeability, at multiple scales is challenging as a result of the combined complexity of the pore structures and fluid physics—in particular, confinement effects which vary across the nanoscale to the microscale. While numerical simulation is possible, the computational cost is prohibitive for realistic domains, which are large and complex. Although machine learning (ML) models have been proposed to circumvent simulation, none so far has simultaneously accounted for heterogeneous 3D structures, fluid confinement effects, and multiple simulation resolutions. By utilizing numerous computer science techniques to improve the scalability of training, we have for the first time developed a general flow model that accounts for the pore-structure and corresponding physical phenomena at scales from Angstrom to the micrometer. Using synthetic computational domains for training, our ML model exhibits strong performance (R 2 = 0.9) when tested on extremely diverse real domains at multiple scales.
FleCSI Project
This report discusses the structure of computational science.
Activities of the Institute for Computer Applications in Science and Engineering (ICASE)
This report summarizes research conducted at the Institute for Computer Applications Science and Engineering in applied mathematics, numerical analysis, and computer science during the period October 2, 1987 through March 31, 1988.
Building a Diverse and Inclusive HPC Community for Mission-Driven Team Science
The U.S. Department of Energy (DOE) has been a long-standing leader in driving advances in science and technology through advanced computing. However, DOE laboratories are currently facing urgent workforce challenges, particularly in terms of underrepresentation from key communities, including people of color, women, persons with disabilities, and first-generation scholars. This paper introduces the work carried out as part of the Exascale Computing Project (ECP) Broadening Participation Initiative, which aims to address workforce challenges through a lens that considers the distinct needs and culture of high-performance computing (HPC). The work focuses on three main efforts: hosting Intro to HPC Bootcamps, expanding the Sustainable Research Pathways (SRP) internship and workforce development program, and establishing an HPC Workforce Development and Retention Action Group. Finally, the paper also highlights various workforce efforts throughout the computational science community and explores opportunities for future work aimed at broadening participation in HPC.
Cloud Computing at JPL Science Data Systems
Cloud computing is an approach and tool to support "Big Data".