Results for “Scientific software” — Page 19

NASA NTRS1999

Interdisciplinary Research Scenario Testing of EOSDIS

During the reporting period, the Principle Investigator (PI) has continued to serve on numerous review panels, task forces and committees with the goal of providing input and guidance for the Earth Observing System Data and Information System (EOSDIS) program at NASA Headquarters and NASA GSFC. In addition, the PI has worked together with personnel at the University of Virginia and the subcontractor (Simpson Weather Associates (SWA)) to continue to evaluate the latest releases of various versions of the user interfaces to the EOSDIS. Finally, as part of the subcontract, SWA has created an on-line Hierarchial Data Format (HDF) tutorial for non-HDF experts, particularly those that will be using EOSDIS and future EOS data products. A summary of these three activities is provided. The topics include: 1) Participation on EODIS Panels and Committees; 2) Evaluation and Tire Kicking of EODIS User Interfaces; and 3) An On-line HDF Tutorial. The report also includes attachments A, B, and C. Attachment A: Report From the May 1999 Science Data Panel. The topics include: 1) Summary of Data Panel Meeting; and 2) Panel's Comments/Recommendations. Attachment B: Survey Requesting Integrated Design Systems (IDS) Teams Input on the Descoping and Rescoping of the EODIS; and Attachment C: An HDF Tutorial for Beginners: EODIS Users and Small Data Providers (HTML Version). The topics include: 1) Tutorial Overview; 2) An introduction to HDF; 3) The HDF Library: Software and Hardware; 4) Methods of Working with HDF Files; 5) Scientific Data API; 6) Attributes and Metadata; 7) Writing a SDS to an HDF file; 8) Obtaining Information on Existing HDF Files; 9) Reading a Scientific Data Set from an HDF file: 10) Example Programs; 11) Browsing and Visualizing HDF Data; and 12) Laboratory (Question and Answer).

Emmitt, G. D.↗

NASA NTRS1999

Examining Mars with SPICE

The International Mars Conference highlights the wealth of scientific data now and soon to be acquired from an international armada of Mars-bound robotic spacecraft. Underlying the planning and interpretation of these scientific observations around and upon Mars are ancillary data and associated software needed to deal with trajectories or locations, instrument pointing, timing and Mars cartographic models. The NASA planetary community has adopted the SPICE system of ancillary data standards and allied tools to fill the need for consistent, reliable access to these basic data and a near limitless range of derived parameters. After substantial rapid growth in its formative years, the SPICE system continues to evolve today to meet new needs and improve ease of use. Adaptations to handle landers and rovers were prototyped on the Mars pathfinder mission and will next be used on Mars '01-'05. Incorporation of new methods to readily handle non-inertial reference frames has vastly extended the capability and simplified many computations. A translation of the SPICE Toolkit software suite to the C language has just been announced. To further support cartographic calculations associated with Mars exploration the SPICE developers at JPL have recently been asked by NASA to work with cartographers to develop standards and allied software for storing and accessing control net and shape model data sets; these will be highly integrated with existing SPICE components. NASA specifically supports the widest possible utilization of SPICE capabilities throughout the international space science community. With NASA backing the Russian Space Agency and Russian Academy of Science adopted the SPICE standards for the Mars 96 mission. The SPICE ephemeris component will shortly become the international standard for agencies using the Deep Space Network. U.S. and European scientists hope that ESA will employ SPICE standards on the Mars Express mission. SPICE is an open set of standards, and all related specifications and software are freely distributed around the world. This poster describes the current state of SPICE system development, with special emphasis on current and planned support for Mars exploration missions.

Acton, Charles H.↗

NASA NTRS2002

Using EventScope with Mars Mission Data to Create Geoscience Curricula

EventScope is an educational software application that incorporates Mars mission data to teach geologic and scientific concepts using 3D (three-dimensional) models of planetary surfaces. It serves as a supplement to middle school science curricula. Additional information is contained in the original extended abstract.

McElfresh, S. B. Z.↗

NASA NTRS2022

Fermi Gamma-ray Space Telescope

The Fermi Gamma-ray Space Telescope, a key mission in multiwavelength and multimessenger studies, has been surveying the γ-ray sky from its low-Earth orbit since 2008. Its two scientific instruments, the Gamma-ray Burst Monitor (GBM) and the Large Area Telescope (LAT), cover 8 orders of magnitude in photon energy. The GBM consists of 12 Sodium Iodide detectors and 2 Bismuth Germinate detectors, covering the 10 keV - 40 MeV energy range, arrayed on two sides of the spacecraft so as to view the entire sky that is not occulted by the Earth. The LAT is a pair production telescope based on silicon strip trackers, a Cesium Iodide calorimeter, and a plastic scintillator anticoincidence system. It covers the energy range from about 20 MeV to more than 500 GeV, with a field of view of about 2.4 steradians. Thanks to their huge fields of view, the instruments can observe the entire sky with a cadence of about an hour for GBM and about three hours for LAT. All γ-ray data from Fermi become public immediately, enabling a broad range of multiwavelength and multimessenger research. Over 3000 γ-ray bursts (GRBs), including GRB 170817A associated with a neutron star merger detected in gravitational waves, and 5000 high-energy sources, including the blazar TXS 0506+056 associated with high-energy neutrinos, have been detected by the Fermi instruments. The Fermi Science Support Center provides a wide array of resources to enable scientific use of the data, including background models, source catalogs, analysis software, documentation, and a Help Desk.

gamma rays↗

NASA NTRS2022

Evaluating Network Performance of Containerized Test Framework for Distributed Space Systems

Distributed space systems are a mission architecture consisting of multiple spacecraft as a cohesive system which provide multipoint sampling, increased mission coverage, or improved sample resolution, while reducing mission risk through redundancy. To fully realize the potential of these systems, eventually scaling to hundreds or thousands of spacecraft, distributed space systems need to be operated as a single entity, which will enable a variety of novel scientific space missions. The Distributed Spacecraft Autonomy (DSA) project is a software project which aims to mature the technology needed for those systems, namely autonomous decision-making and swarm networking. The DSA project leverages a containerized swarm test framework to simulate spacecraft software, which can identify emergent behavior early in development. Container virtualization allows distributed spacecraft systems to be simulated entirely in software on a single computer, avoiding the overhead associated with conventional approaches like hardware facsimiles and virtual machines. For this approach to be effective, the simulated system behavior must not be artificially influenced by the swarm test framework itself. To address this, we present a series of benchmarks to quantify virtual network bandwidth available on a single-host computer and contextualize this against the network and application behavior of the DSA swarm test framework.

Distributed Spacecraft Autonomy↗

DOE OSTI2025

A2SD: Accelerating Scientific Innovation Through Autonomous Discovery Systems

The 2025 Advancing Autonomous Scientific Discovery (A2SD) workshop convened researchers from academia, national laboratories, and industry to explore the transformative role of autonomy in scientific discovery. The workshop highlighted a convergence of artificial intelligence, robotics, and computational workflows into autonomous systems capable of accelerating the scientific process. Presentations and discussions spanned autonomous experimentation, intelligent workflow orchestration, digital twins, and agent-based systems for managing complex research ecosystems. Key challenges discussed included interoperability across heterogeneous infrastructures, near real-time data management under FAIR principles, reproducibility, and the integration of human oversight. The workshop also emphasized the need for modular software interfaces, federated learning models, and education initiatives to support a next-generation scientific workforce.

Taufer, Michela [University of Tennessee, Knoxvill↗

NASA NTRSDate not supplied

The Science Discovery Engine: Connecting Heterogeneous Scientific Data and Information

Transformative science often occurs at the boundaries of different disciplines. Making interdisciplinary science data, software and documentation discoverable and accessible is essential to enabling transformative science. However, connecting this diverse and heterogeneous information is often a challenge due to several factors including the dispersed and sometimes isolated nature of data and the semantic differences between topical areas. NASA’s Science Discovery Engine (SDE) has developed several approaches to tackling these challenges. The SDE is a unified, insightful search experience that enables discovery of NASA’s open science data across five topical areas: astrophysics, biological and physical sciences, Earth science, heliophysics and planetary science. In this presentation, we will discuss our efforts to develop a systematic scientific curation workflow to integrate diverse content into a single search environment. We will also share lessons learned from our work to create a metadata crosswalk across the five disciplines.

Kaylin Bugbee↗

NASA NTRSDate not supplied

Production of Flight Instruments for Multi-Satellite Constellations

Recent National Academy of Science Decadal Surveys in space and Earth-science have called for simultaneous, distributed multi-point measurements in and from space, requiring constellations of small spacecraft. Small satellites have demonstrated their utility for enabling high-quality science measurements and observations. NASA missions have leveraged advances in sensor miniaturization, technology innovations, and new small satellite mission architectures to enable meaningful measurement-based scientific investigations that operate on small satellites and that are responsive to science objectives described in National Academy of Science Decadal Surveys. The advent of high capability small spacecraft enables consideration of science missions involving multiple small spacecraft, constellations of a few or many for simultaneous distributed in situ observations or remote observations from a variety of viewpoints. The central challenge for fielding instrumented space-flight constellations is to provide the required multiple sets of fully verified and calibrated instrument hardware, software, and operational processes from within a one-off project-based scientific space flight culture. Although there are numerous commercial entities providing “off-the shelf” spacecraft and avionics, the challenge lies in the multi-unit production of the uniquely targeted instrumentation necessary to perform the specific measurements required for a particular science investigation. Traditionally, the cost of such instrumentation has represented a significant portion of the hardware cost for a mission and posed the highest risk area for implementation. A shift in paradigm from large science platforms to constellations of smaller satellites drives the challenge to build instruments in a quasi-production environment. This paper describes key aspects of, and challenges encountered in the development program for the successful production of instruments for the Fast-Plasma Investigation (FPI) instrument suite of 64 flight plasma spectrometers and supporting electronics on the NASA Magnetospheric Multiscale (MMS) 4 satellite constellation mission. Although the MMS mission was not composed of small satellites, there are key aspects of the instrument production that apply directly to constellations of small satellites.

Arthur D Jacques↗

NASA NTRSDate not supplied

Production of Flight Instruments for Multi-Satellite Constellations

Recent National Academy of Science Decadal Surveys in space and Earth-science have called for simultaneous, distributed multi-point measurements in and from space, requiring constellations of small spacecraft. Small satellites have demonstrated their utility for enabling high-quality science measurements and observations. NASA missions have leveraged advances in sensor miniaturization, technology innovations, and new small satellite mission architectures to enable meaningful measurement-based scientific investigations that operate on small satellites and that are responsive to science objectives described in National Academy of Science Decadal Surveys. The advent of high capability small spacecraft enables consideration of science missions involving multiple small spacecraft, constellations of a few or many for simultaneous distributed in situ observations or remote observations from a variety of viewpoints. The central challenge for fielding instrumented space-flight constellations is to provide the required multiple sets of fully verified and calibrated instrument hardware, software, and operational processes from within a one-off project-based scientific space flight culture. Although there are numerous commercial entities providing “off-the shelf” spacecraft and avionics, the challenge lies in the multi-unit production of the uniquely targeted instrumentation necessary to perform the specific measurements required for a particular science investigation. Traditionally, the cost of such instrumentation has represented a significant portion of the hardware cost for a mission and posed the highest risk area for implementation. A shift in paradigm from large science platforms to constellations of smaller satellites drives the challenge to build instruments in a quasi-production environment. This paper describes key aspects of, and challenges encountered in the development program for the successful production of instruments for the Fast-Plasma Investigation (FPI) instrument suite of 64 flight plasma spectrometers and supporting electronics on the NASA Magnetospheric Multiscale (MMS) 4 satellite constellation mission. Although the MMS mission was not composed of small satellites, there are key aspects of the instrument production that apply directly to constellations of small satellites.

Arthur D Jacques↗

DOE OSTI2025

Hybrid learning techniques for scientific data reduction with performance guarantees

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence

The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

HydraGNN v5.0

HydraGNN v5.0 expands the code base into a more portable, scalable, and flexible framework for scientific graph learning, with particular strength in atomistic machine-learning interatomic potentials and large-scale distributed training. The release adds Fully Sharded Data Parallel (FSDP) support alongside existing DDP and DeepSpeed paths, including FSDP-aware checkpointing and optimizer integration, and introduces a configurable multi-precision training workflow supporting FP32, BF16, and FP64 across GPUs and Intel XPUs. For atomistic modeling, HydraGNN v5.0 strengthens its MLIP capabilities through dynamic graph construction at every forward pass, energy-conserving force prediction via automatic differentiation, and per-atom energy loss formulations, while extending EGNN models to properly handle periodic boundary conditions. The release also broadens model expressiveness through graph-level attribute conditioning, adds new multi-task and model-parallel extensions such as MACE support and encoder/decoder branch optimization, and expands application coverage with integrated examples for datasets including OC25, Nabla2-DFT, QCML, Open Polymers 2026, and OPF. In parallel, HydraGNN v5.0 improves production readiness through performance optimizations for large-scale runs, stratified sampling and linear-regression preprocessing utilities, and tested installation scripts for DOE supercomputers including Frontier, Aurora, Perlmutter, and Andes. Overall, the release advances HydraGNN as a robust software platform for scalable graph neural networks across materials science, chemistry, and scientific machine learning workflows

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗

NASA NTRS1994

Scientific investigations of atmospheric processes

Research was performed in atmospheric, dynamical, and thermodynamical processes and in other disciplines necessary to accomplish the following tasks: develop procedures for combining generalized radiative transfer codes with dynamic atmospheric model codes; perform diagnostic analysis of atmospheric processes to gain a better understanding of the evolution and development of mesoscale circulation systems and their precipitation structures; and to develop algorithms and software necessary to graphically display diagnostic sets on the MSFC McIDAS and EADS to facilitate scientific study and sensor capability evaluation. Research activities during this reporting period are detailed.

Source record↗

NASA NTRS1995

Scientific Visualization and Computational Science: Natural Partners

Scientific visualization is developing rapidly, stimulated by computational science, which is gaining acceptance as a third alternative to theory and experiment. Computational science is based on numerical simulations of mathematical models derived from theory. But each individual simulation is like a hypothetical experiment; initial conditions are specified, and the result is a record of the observed conditions. Experiments can be simulated for situations that can not really be created or controlled. Results impossible to measure can be computed.. Even for observable values, computed samples are typically much denser. Numerical simulations also extend scientific exploration where the mathematics is analytically intractable. Numerical simulations are used to study phenomena from subatomic to intergalactic scales and from abstract mathematical structures to pragmatic engineering of everyday objects. But computational science methods would be almost useless without visualization. The obvious reason is that the huge amounts of data produced require the high bandwidth of the human visual system, and interactivity adds to the power. Visualization systems also provide a single context for all the activities involved from debugging the simulations, to exploring the data, to communicating the results. Most of the presentations today have their roots in image processing, where the fundamental task is: Given an image, extract information about the scene. Visualization has developed from computer graphics, and the inverse task: Given a scene description, make an image. Visualization extends the graphics paradigm by expanding the possible input. The goal is still to produce images; the difficulty is that the input is not a scene description displayable by standard graphics methods. Visualization techniques must either transform the data into a scene description or extend graphics techniques to display this odd input. Computational science is a fertile field for visualization research because the results vary so widely and include things that have no known appearance. The amount of data creates additional challenges for both hardware and software systems. Evaluations of visualization should ultimately reflect the insight gained into the scientific phenomena. So making good visualizations requires consideration of characteristics of the user and the purpose of the visualization. Knowledge about human perception and graphic design is also relevant. It is this breadth of knowledge that stimulates proposals for multidisciplinary visualization teams and intelligent visualization assistant software. Visualization is an immature field, but computational science is stimulating research on a broad front.

Uselton, Samuel P.↗

NASA NTRS2007

Enhanced Master Controller Unit Tester

The Enhanced Master Controller Unit Tester (EMUT) software is a tool for development and testing of software for a master controller (MC) flight computer. The primary function of the EMUT software is to simulate interfaces between the MC computer and external analog and digital circuitry (including other computers) in a rack of equipment to be used in scientific experiments. The simulations span the range of nominal, off-nominal, and erroneous operational conditions, enabling the testing of MC software before all the equipment becomes available.

Benson, Patricia↗

DOE OSTI2025

SF-25-047 IMAGEMARKER

Image Marker is a tool for marking, categorizing, and annotating TIFF, FITS, PNG, and JPEG files. The software is intended to facilitate crowd sourcing human classification of features in scientific images without having to use web tools.

BLEEM, LINDSEYE [Argonne National Laboratory (ANL)↗

DOE OSTI2025

An HPC benchmark survey and taxonomy for characterization

The field of High-Performance Computing (HPC) is defined by providing computing devices with highest performance for a variety of demanding scientific users. The tight co-design relationship between HPC providers and users propels the field forward, paired with technological improvements, achieving continuously higher performance and resource utilization. A key device for system architects, architecture researchers, and scientific users are benchmarks, allowing for well-defined assessment of hardware, software, and algorithms. Many benchmarks exist in the community, from individual niche benchmarks testing specific features, to large-scale benchmark suites for whole procurements. We survey the available HPC benchmarks, summarizing them in table form with key details and concise categorization, also through an interactive website. For categorization, we present a benchmark taxonomy for well-defined characterization of benchmarks.

Benchmarking↗

NASA NTRS1980

Spacelab data analysis and interactive control study

The study consisted of two main tasks, a series of interviews of Spacelab users and a survey of data processing and display equipment. Findings from the user interviews on questions of interactive control, downlink data formats, and Spacelab computer software development are presented. Equipment for quick look processing and display of scientific data in the Spacelab Payload Operations Control Center (POCC) was surveyed. Results of this survey effort are discussed in detail, along with recommendations for NASA development of several specific display systems which meet common requirements of many Spacelab experiments.

Tarbell, T. D.↗