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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

GMT: A deep learning approach to generalized multivariate translation for scientific data analysis and visualization

In scientific visualization, despite the significant advances of deep learning for data generation, researchers have not thoroughly investigated the issue of data translation. We present a new deep learning approach called generalized multivariate translation (GMT) for multivariate time-varying data analysis and visualization. Like V2V, GMT assumes a preprocessing step that selects suitable variables for translation. However, unlike V2V, which only handles one-to-one variable translation during training and inference, GMT enables one-to-many and many-to-many variable translation in the same framework. We leverage the recent StarGAN design from multi-domain image-to-image translation to achieve this generalization capability. We experiment with different loss functions and injection strategies to explore the best choices and leverage pre-training for performance improvement. We compare GMT with other state-of-the-art methods (i.e., Pix2Pix, V2V, StarGAN). Furthermore, the results demonstrate the overall advantage of GMT in translation quality and generalization ability.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Integrating HPC, AI, and Workflows for Scientific Data Analysis: Report from Dagstuhl Seminar 23352

The Dagstuhl Seminar 23352, titled “Integrating HPC, AI, and Workflows for Scientific Data Analysis,” held from August 27 to September 1, 2023, was a significant event focusing on the synergy between High-Performance Computing (HPC), Artificial Intelligence (AI), and scientific workflow technologies. The seminar recognized that modern Big Data analysis in science rests on three pillars: workflow technologies for reproducibility and steering, AI and Machine Learning (ML) for versatile analysis, and HPC for handling large data sets. These elements, while crucial, have traditionally been researched separately, leading to gaps in their integration. The seminar aimed to bridge these gaps, acknowledging the challenges and opportunities at the intersection of these technologies. The event highlighted the complex interplay between HPC, workflows, and ML, noting how ML has increasingly been integrated into scientific workflows, thereby enhancing resource demands and bringing new requirements to HPC architectures, like support for GPUs and iterative computations. The seminar also addressed the challenges in adapting HPC for large-scale ML tasks, including in areas like deep learning, and the need for workflow systems to evolve to leverage ML in data analysis fully. Moreover, the seminar explored how ML could optimize scientific workflow systems and HPC operations, such as through improved scheduling and fault tolerance. A key focus was on identifying prestigious use cases of ML in HPC and understanding their unique, unmet requirements. The stochastic nature of ML and its impact on the reproducibility of data analysis on HPC systems was also a topic of discussion.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

The Infrared Astronomical Satellite /IRAS/ Scientific Data Analysis System /SDAS/ sky flux subsystem

The sky flux subsystem of the Infrared Astronomical Satellite Scientific Data Analysis System is described. Its major output capabilities are (1) the all-sky lune maps (8-arcminute pixel size), (2) galactic plane maps (2-arcminute pixel size) and (3) regional maps of small areas such as extended sources greater than 1-degree in extent. The major processing functions are to (1) merge the CRDD and pointing data, (2) phase the detector streams, (3) compress the detector streams in the in-scan and cross-scan directions, and (4) extract data. Functional diagrams of the various capabilities of the subsystem are given. Although this device is inherently nonimaging, various calibrated and geometrically controlled imaging products are created, suitable for quantitative and qualitative scientific interpretation.

Stagner, J. R.↗
DOE OSTI2017

Improving Scientific Data Analysis Through Multi-touch Enabled Interactive Data Visualization with Applications to Neutron Science

Scientific data analysis is an inherently exploratory process that requires flexible human interaction with data visualization and anal- ysis tools. In this paper, we describe challenges in neutron scattering data analysis and present our approach for allowing more effective human interaction. In addition to multi-touch enabled data visualiza- tion techniques, we describe color scale enhancements, layouts, and visual analytics approaches for 3D volume slicing. Since challenges in neutron science are echoed in most scientific fields, we generalize the project objective and present a case for bridging the gap between the latest advances in interactive data visualization and practical scientific scenarios where new critical knowledge is buried in large volumes of complex data.

Steed, Chad↗
DOE OSTI2025

Understanding and Estimating Error Propagation in Neural Networks for Scientific Data Analysis

Neural networks are increasingly integrated into scientific discovery, where input data reduction and model quantization play a key role in accelerating inference. However, understanding and mitigating the impact of these techniques on output error is critical for ensuring reliable results, particularly in tasks demanding high numerical precision. This paper introduces a comprehensive framework for optimizing neural network inference in scientific computing by combining data reduction and weight quantization while maintaining error-controlled outcomes. We develop theoretical analyses to bound error propagation under these reductions and propose a framework that balances computational performance with error constraints. Evaluation on real-world learning-based combustion simulations and satellite image classification demonstrates that our derived error bounds accurately predict observed errors while enabling significant computational speedup under our framework. This work highlights the potential for further leveraging advancements in modern lossy compression algorithms and hardware accelerators that support lower-precision formats.

He, Weiming [New Jersey Institute of Technology]↗
NASA NTRS1980

Infrared Astronomical Satellite /IRAS/ Scientific Data Analysis System

The Infrared Astronomical Satellite (IRAS), to be launched in 1982, is discussed. It will systematically survey the entire sky over a large percentage of the infrared spectrum, in the wavelength region of 8 to 120 microns, at sensitivities a hundred times greater than previously achieved from high-altitude observatories, aircraft, balloons or sounding rockets. The Scientific Data Analysis System (SDAS), an off-line data processing facility, is examined. Its primary function is to produce a catalog of inertially fixed infrared-emitting point sources (mainly stars and galaxies) observed during the IRAS survey. Details for source detection and confirmation are given. It is estimated that the catalog will contain approximately a million objects having a brightness of 10 amtowatts per square centimeter or greater; 125,000 SDAS detections, if spurious events of signal-to-noise ratios greater than 2.5 are included, will be made every day.

Duxbury, J. H.↗
NASA NTRS1998

AXAF: Current Developments and Future Plans for the Scientific Data Analysis

AXAF, the Advanced X-ray Astrophysics Facility, is one of NASA's great observatories scheduled for launch in January of 1999 by a Space Shuttle. This facility will be available to scientists in the United States and to the international astronomical community over an anticipated mission lifetime of at least 5 years. The objective of AXAF is to make astrophysical observations in the 0.09 to 10.0 keV energy range with two imaging detectors, and two sets of transmission gratings giving high spatial (0.5 arcsec) and spectral (E/(Delta)E=100-2000) resolution. The AXAF Science Center (ASC) in Cambridge, MA, USA is responsible for obtaining and reviewing observation proposals submitted by users, providing long range science planning and performing all science data calibration, data analysis, science instrument trend analysis, and science data archiving. The ASC will distribute science data products to the users. We will review here the current developments and future plans for the AXAF scientific data analysis system, its infrastructure and new key features, including the global modelling and fitting environment, integrated data analysis GUIs and new analysis tools.

Fruscione, Antonella↗
NASA NTRS1993

Space and Earth Sciences, Computer Systems, and Scientific Data Analysis Support, Volume 1

This Final Progress Report covers the specific technical activities of Hughes STX Corporation for the last contract triannual period of 1 June through 30 Sep. 1993, in support of assigned task activities at Goddard Space Flight Center (GSFC). It also provides a brief summary of work throughout the contract period of performance on each active task. Technical activity is presented in Volume 1, while financial and level-of-effort data is presented in Volume 2. Technical support was provided to all Division and Laboratories of Goddard's Space Sciences and Earth Sciences Directorates. Types of support include: scientific programming, systems programming, computer management, mission planning, scientific investigation, data analysis, data processing, data base creation and maintenance, instrumentation development, and management services. Mission and instruments supported include: ROSAT, Astro-D, BBXRT, XTE, AXAF, GRO, COBE, WIND, UIT, SMM, STIS, HEIDI, DE, URAP, CRRES, Voyagers, ISEE, San Marco, LAGEOS, TOPEX/Poseidon, Pioneer-Venus, Galileo, Cassini, Nimbus-7/TOMS, Meteor-3/TOMS, FIFE, BOREAS, TRMM, AVHRR, and Landsat. Accomplishments include: development of computing programs for mission science and data analysis, supercomputer applications support, computer network support, computational upgrades for data archival and analysis centers, end-to-end management for mission data flow, scientific modeling and results in the fields of space and Earth physics, planning and design of GSFC VO DAAC and VO IMS, fabrication, assembly, and testing of mission instrumentation, and design of mission operations center.

Estes, Ronald H.↗
DOE OSTI2024

Immersive Visualization for Scientific Data Analysis

We will present the use of immersive visualization at the National Renewable Energy Laboratory (NREL), showcasing how immersive visualization is advancing scientific research and engineering practices and transforming our day-to-day operations. We are leveraging immersive visualization to support scientific discovery and engineering in various domains, including material design, computational fluid dynamics, immersive analytics, grid modernization, digital twins, and situated visualization. We have observed several benefits across four key areas: enhanced spatial judgments, improved understanding through interaction, increased capacity to embed high-dimensional data, and improved collaboration.

immersive analytics↗
DOE OSTI2023

Advanced Visualization for Scientific Data Analysis and Insight [Slides]

This talk will explore how we have used advanced visualization technologies to support analytical reasoning and knowledge discovery. Specifically, we will present several examples detailing some recent scientific successes using state-of-the-art immersive and high-resolution visualization at the National Renewable Energy Laboratory's Computational Science Center. On multiple occasions, we have observed scientists and engineers discover features in their data using advanced visualization technologies that they had not seen in prior investigations of their data on traditional desktop displays. We have embedded more information into our analytics tools, allowing engineers to explore complex multivariate spaces. We have observed how interactions seem to catalyze understanding.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Advanced Inversion Algorithms for Scientific Data Analysis [Slides]

Accurate subsurface characterization is crucial for all subsurface energy exploration. Accurate characterization of uncertain subsurface properties is also critical for monitoring storage of CO 2 , estimating pathways of subsurface contaminant transport, and monitoring potential nuclear explosions for treaty verification. This research will advance our world-leading subsurface sensing capabilities that are crucial for LANL missions in energy security (geothermal energy, oil/gas resource exploration, geologic carbon storage) and nuclear security (facility monitoring, detonation detection).

47 OTHER INSTRUMENTATION↗
NASA NTRS2000

Scientific Data Analysis and Software Support: Geodynamics

The support on this contract centers on development of data analysis strategies, geodynamic models, and software codes to study four-dimensional geodynamic and oceanographic processes, as well as studies and mission support for near-Earth and interplanetary satellite missions. SRE had a subcontract to maintain the optical laboratory for the LTP, where instruments such as MOLA and GLAS are developed. NVI performed work on a Raytheon laser altimetry task through a subcontract, providing data analysis and final data production for distribution to users. HBG had a subcontract for specialized digital topography analysis and map generation. Over the course of this contract, Raytheon ITSS staff have supported over 60 individual tasks. Some tasks have remained in place during this entire interval whereas others have been completed and were of shorter duration. Over the course of events, task numbers were changed to reflect changes in the character of the work or new funding sources. The description presented below will detail the technical accomplishments that have been achieved according to their science and technology areas. What will be shown is a brief overview of the progress that has been made in each of these investigative and software development areas. Raytheon ITSS staff members have received many awards for their work on this contract, including GSFC Group Achievement Awards for TOPEX Precision Orbit Determination and the Joint Gravity Model One Team. NASA JPL gave the TOPEX/POSEIDON team a medal commemorating the completion of the primary mission and a Certificate of Appreciation. Raytheon ITSS has also received a Certificate of Appreciation from GSFC for its extensive support of the Shuttle Laser Altimeter Experiment.

Klosko, Steven↗
NASA NTRS1994

LinkWinds, A System for Interactive Scientific Data Analysis and Visualization

The Linked Windows Interactive Data System is a prototype visual data exploration system resulting from a NASA/JPL program of research into graphical methods for rapidly and interactively accessing, displaying and analyzing large multivariate multidisciplinary data sets.

dynamic interconnection data-linking paradigm easy↗