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NASA NTRSDate not supplied

ML/AI Applications to the Atmosphere Science Data and Simulations (Demonstration and Vision)

Artificial Intelligence has been recognized as one of the most powerful tools for scientific research. It has a wide range of applications in atmospheric science and plays a significant role in advancing our understanding of the Earth-Atmosphere system, as well as improving our ability to monitor, predict, and respond to atmospheric observations and simulations. Different AI architectures can help us handle various problems according to our research goals. In this presentation, we will introduce several use cases and models for applying computer vision to active remote sensing data from CALIOP, analyzing cloud dynamical relationships for climate model parameterizations, and building surrogate models for lidar simulations. The presentation will highlight the advantages that AI offers compared to traditional analytical methods. By assembling pieces of the multidimensional atmospheric science puzzle, we can integrate our knowledge from observations to simulations.

Shan Zeng↗

NASA NTRS2002

Software Engineering Support of the Third Round of Scientific Grand Challenge Investigations: Earth System Modeling Software Framework Survey

One of the most significant challenges in large-scale climate modeling, as well as in high-performance computing in other scientific fields, is that of effectively integrating many software models from multiple contributors. A software framework facilitates the integration task, both in the development and runtime stages of the simulation. Effective software frameworks reduce the programming burden for the investigators, freeing them to focus more on the science and less on the parallel communication implementation. while maintaining high performance across numerous supercomputer and workstation architectures. This document surveys numerous software frameworks for potential use in Earth science modeling. Several frameworks are evaluated in depth, including Parallel Object-Oriented Methods and Applications (POOMA), Cactus (from (he relativistic physics community), Overture, Goddard Earth Modeling System (GEMS), the National Center for Atmospheric Research Flux Coupler, and UCLA/UCB Distributed Data Broker (DDB). Frameworks evaluated in less detail include ROOT, Parallel Application Workspace (PAWS), and Advanced Large-Scale Integrated Computational Environment (ALICE). A host of other frameworks and related tools are referenced in this context. The frameworks are evaluated individually and also compared with each other.

Talbot, Bryan↗

NASA NTRS2000

Architectural Methodology Report

The establishment of conventions between two communicating entities in the end systems is essential for communications. Examples of the kind of decisions that need to be made in establishing a protocol convention include the nature of the data representation, the for-mat and the speed of the date representation over the communications path, and the sequence of control messages (if any) which are sent. One of the main functions of a protocol is to establish a standard path between the communicating entities. This is necessary to create a virtual communications medium with certain desirable characteristics. In essence, it is the function of the protocol to transform the characteristics of the physical communications environment into a more useful virtual communications model. The final function of a protocol is to establish standard data elements for communications over the path; that is, the protocol serves to create a virtual data element for exchange. Other systems may be constructed in which the transferred element is a program or a job. Finally, there are special purpose applications in which the element to be transferred may be a complex structure such as all or part of a graphic display. NASA's Glenn Research Center (GRC) defines and develops advanced technology for high priority national needs in communications technologies for application to aeronautics and space. GRC tasked Computer Networks and Software Inc. (CNS) to describe the methodologies used in developing a protocol architecture for an in-space Internet node. The node would support NASA:s four mission areas: Earth Science; Space Science; Human Exploration and Development of Space (HEDS); Aerospace Technology. This report presents the methodology for developing the protocol architecture. The methodology addresses the architecture for a computer communications environment. It does not address an analog voice architecture.

Dhas, Chris↗

NASA NTRS2000

Trade-Off Analysis Report

NASAs Glenn Research Center (GRC) defines and develops advanced technology for high priority national needs in communications technologies for application to aeronautics and space. GRC tasked Computer Networks and Software Inc. (CNS) to examine protocols and architectures for an In-Space Internet Node. CNS has developed a methodology for network reference models to support NASAs four mission areas: Earth Science, Space Science, Human Exploration and Development of Space (REDS), Aerospace Technology. CNS previously developed a report which applied the methodology, to three space Internet-based communications scenarios for future missions. CNS conceptualized, designed, and developed space Internet-based communications protocols and architectures for each of the independent scenarios. GRC selected for further analysis the scenario that involved unicast communications between a Low-Earth-Orbit (LEO) International Space Station (ISS) and a ground terminal Internet node via a Tracking and Data Relay Satellite (TDRS) transfer. This report contains a tradeoff analysis on the selected scenario. The analysis examines the performance characteristics of the various protocols and architectures. The tradeoff analysis incorporates the results of a CNS developed analytical model that examined performance parameters.

Dhas, Chris↗

NASA NTRS2000

Protocol Architecture Model Report

NASA's Glenn Research Center (GRC) defines and develops advanced technology for high priority national needs in communications technologies for application to aeronautics and space. GRC tasked Computer Networks and Software Inc. (CNS) to examine protocols and architectures for an In-Space Internet Node. CNS has developed a methodology for network reference models to support NASA's four mission areas: Earth Science, Space Science, Human Exploration and Development of Space (REDS), Aerospace Technology. This report applies the methodology to three space Internet-based communications scenarios for future missions. CNS has conceptualized, designed, and developed space Internet-based communications protocols and architectures for each of the independent scenarios. The scenarios are: Scenario 1: Unicast communications between a Low-Earth-Orbit (LEO) spacecraft inspace Internet node and a ground terminal Internet node via a Tracking and Data Rela Satellite (TDRS) transfer; Scenario 2: Unicast communications between a Low-Earth-Orbit (LEO) International Space Station and a ground terminal Internet node via a TDRS transfer; Scenario 3: Multicast Communications (or "Multicasting"), 1 Spacecraft to N Ground Receivers, N Ground Transmitters to 1 Ground Receiver via a Spacecraft.

Dhas, Chris↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

space biology↗

NASA NTRS1994

Mobile Computing for Aerospace Applications

The use of commercial computer technology in specific aerospace mission applications can reduce the cost and project cycle time required for the development of special-purpose computer systems. Additionally, the pace of technological innovation in the commercial market has made new computer capabilities available for demonstrations and flight tests. Three areas of research and development being explored by the Portable Computer Technology Project at NASA Ames Research Center are the application of commercial client/server network computing solutions to crew support and payload operations, the analysis of requirements for portable computing devices, and testing of wireless data communication links as extensions to the wired network. This paper will present computer architectural solutions to portable workstation design including the use of standard interfaces, advanced flat-panel displays and network configurations incorporating both wired and wireless transmission media. It will describe the design tradeoffs used in selecting high-performance processors and memories, interfaces for communication and peripheral control, and high resolution displays. The packaging issues for safe and reliable operation aboard spacecraft and aircraft are presented. The current status of wireless data links for portable computers is discussed from a system design perspective. An end-to-end data flow model for payload science operations from the experiment flight rack to the principal investigator is analyzed using capabilities provided by the new generation of computer products. A future flight experiment on-board the Russian MIR space station will be described in detail including system configuration and function, the characteristics of the spacecraft operating environment, the flight qualification measures needed for safety review, and the specifications of the computing devices to be used in the experiment. The software architecture chosen shall be presented. An analysis of the performance characteristics of wireless data links in the spacecraft environment will be discussed. Network performance and operation will be modeled and preliminary test results presented. A crew support application will be demonstrated in conjunction with the network metrics experiment.

Alena, Richard↗

NASA NTRS2000

Nanotechnology at NASA Ames

Advanced miniaturization, a key thrust area to enable new science and exploration missions, provides ultrasmall sensors, power sources, communication, navigation, and propulsion systems with very low mass, volume, and power consumption. Revolutions in electronics and computing will allow reconfigurable, autonomous, 'thinking' spacecraft. Nanotechnology presents a whole new spectrum of opportunities to build device components and systems for entirely new space architectures: (1) networks of ultrasmall probes on planetary surfaces; (2) micro-rovers that drive, hop, fly, and burrow; and (3) collections of microspacecraft making a variety of measurements.

Srivastava, Deepak↗

NASA NTRS1991

AIAA/NASA International Symposium on Space Information Systems, 2nd, Pasadena, CA, Sept. 17-19, 1990, Proceedings. Vols. 1 & 2

These proceedings overview major space information system projects and lessons learned from current missions. Other topics include the science information system requirements for the 1990s, an information systems design approach for major programs, the technology needs and projections, the standards for space data information systems, the artificial intelligence technology and applications, international interoperability, and spacecraft data systems and architectures advanced communications. Other topics include the software engineering technology and applications, the multimission multidiscipline information system architectures, the distributed planning and scheduling systems and operations, and the computer and information systems architectures. Paper presented include prospects for scientific data analysis systems for solar-terrestrial physics in the 1990s, the Columbus data management system, data storage technologies for the future, the German aerospace research establishment, and launching artificial intelligence in NASA ground systems.

Tavenner, Leslie A.↗

NASA NTRS2026

Hydrology Copilot: A Cloud-Native Ai System for Hydrological Data Analysis

The emergence of AI-driven Earth observation systems promises to broaden access to petabyte-scale geospatial data beyond domain specialists. However, translating this vision into operational scientific infrastructure requires addressing fundamental challenges in data virtualization, code transparency, and domain-specific reasoning. We present Hydrology Copilot, a cloud-native AI framework for natural-language-driven analysis of Earth observation data. To demonstrate operational capabilities at scale, we implement the system using NASA's North American Land Data Assimilation System version 3 (NLDAS-3), which provides surface meteorological forcing and land-surface model output across North and Central America at 1-km resolution, from which drought diagnostics are derived. The system integrates five core contributions: (1) scalable data virtualization using Kerchunk-based cloud optimized access, achieving a 1.5 to 4.6 times improvement in I/O latency across benchmark queries spanning regional single-day extractions (4.6 times speedup) to continental monthly aggregations (1.5 times speedup); (2) transparent code generation through Microsoft Azure AI Foundry agents that expose executable Python workflows for scientific verification; (3) persistent conversational memory enabling multi-turn analytical discourse across sessions; (4) intelligent query validation that enforces dataset boundaries and resolves ambiguous requests before execution; and (5) a multi-agent architecture coordinating query parsing, code generation, and visualization. We evaluate the system through drought-monitoring workflows, demonstrating reliable code generation, accurate results validated against reference computations and the operational U.S. Drought Monitor, and efficient operation across increasingly complex tasks. By bridging natural-language interfaces with rigorous hydrological analysis, Hydrology Copilot advances beyond proof-of-concept demonstrations to provide a deployable framework for operational Earth science applications.

Data virtualization↗

NASA NTRS2003

Computational Aspects of Data Assimilation and the ESMF

The scientific challenge of developing advanced data assimilation applications is a daunting task. Independently developed components may have incompatible interfaces or may be written in different computer languages. The high-performance computer (HPC) platforms required by numerically intensive Earth system applications are complex, varied, rapidly evolving and multi-part systems themselves. Since the market for high-end platforms is relatively small, there is little robust middleware available to buffer the modeler from the difficulties of HPC programming. To complicate matters further, the collaborations required to develop large Earth system applications often span initiatives, institutions and agencies, involve geoscience, software engineering, and computer science communities, and cross national borders.The Earth System Modeling Framework (ESMF) project is a concerted response to these challenges. Its goal is to increase software reuse, interoperability, ease of use and performance in Earth system models through the use of a common software framework, developed in an open manner by leaders in the modeling community. The ESMF addresses the technical and to some extent the cultural - aspects of Earth system modeling, laying the groundwork for addressing the more difficult scientific aspects, such as the physical compatibility of components, in the future. In this talk we will discuss the general philosophy and architecture of the ESMF, focussing on those capabilities useful for developing advanced data assimilation applications.

daSilva, A.↗

NASA NTRS2002

Systems Architecture for Fully Autonomous Space Missions

The NASA Goddard Space Flight Center is working to develop a revolutionary new system architecture concept in support of fully autonomous missions. As part of GSFC's contribution to the New Millenium Program (NMP) Space Technology 7 Autonomy and on-Board Processing (ST7-A) Concept Definition Study, the system incorporates the latest commercial Internet and software development ideas and extends them into NASA ground and space segment architectures. The unique challenges facing the exploration of remote and inaccessible locales and the need to incorporate corresponding autonomy technologies within reasonable cost necessitate the re-thinking of traditional mission architectures. A measure of the resiliency of this architecture in its application to a broad range of future autonomy missions will depend on its effectiveness in leveraging from commercial tools developed for the personal computer and Internet markets. Specialized test stations and supporting software come to past as spacecraft take advantage of the extensive tools and research investments of billion-dollar commercial ventures. The projected improvements of the Internet and supporting infrastructure go hand-in-hand with market pressures that provide continuity in research. By taking advantage of consumer-oriented methods and processes, space-flight missions will continue to leverage on investments tailored to provide better services at reduced cost. The application of ground and space segment architectures each based on Local Area Networks (LAN), the use of personal computer-based operating systems, and the execution of activities and operations through a Wide Area Network (Internet) enable a revolution in spacecraft mission formulation, implementation, and flight operations. Hardware and software design, development, integration, test, and flight operations are all tied-in closely to a common thread that enables the smooth transitioning between program phases. The application of commercial software development techniques lays the foundation for delivery of product-oriented flight software modules and models. Software can then be readily applied to support the on-board autonomy required for mission self-management. An on-board intelligent system, based on advanced scripting languages, facilitates the mission autonomy required to offload ground system resources, and enables the spacecraft to manage itself safely through an efficient and effective process of reactive planning, science data acquisition, synthesis, and transmission to the ground. Autonomous ground systems in turn coordinate and support schedule contact times with the spacecraft. Specific autonomy software modules on-board include mission and science planners, instrument and subsystem control, and fault tolerance response software, all residing within a distributed computing environment supported through the flight LAN. Autonomy also requires the minimization of human intervention between users on the ground and the spacecraft, and hence calls for the elimination of the traditional operations control center as a funnel for data manipulation. Basic goal-oriented commands are sent directly from the user to the spacecraft through a distributed internet-based payload operations "center". The ensuing architecture calls for the use of spacecraft as point extensions on the Internet. This paper will detail the system architecture implementation chosen to enable cost-effective autonomous missions with applicability to a broad range of conditions. It will define the structure needed for implementation of such missions, including software and hardware infrastructures. The overall architecture is then laid out as a common thread in the mission life cycle from formulation through implementation and flight operations.

Esper, Jamie↗

NASA NTRS1999

An Integrated Architecture for Onboard Spacecraft

As increasingly complex scientific and environmental observation spacecraft are deployed, the burden on the downlink assets, and ground-based systems complexity and cost is becoming a major problem. Already, the limitations of communications bandwidth and processing throughput limit the science data gathering, both in volume and in rate. This poses a dilemma to the scientist experimenter forcing choices between data collection and bandwidth/processing/archiving. Advances in ground based processing and space-to-Earth links have fallen behind the requirements for observation data, at increasing rates, over the last few decades. As NASA achieves its 40th anniversary, the ability to observe and capture phenomena of theoretical and practical interest to life on Earth far outstrips the ability to transfer, process, or store these data. NASA recognizes the need to invest on technological advancements that will enable both the space and ground systems to address the limitations. Spacecraft onboard computing power is a clear one. The capability of creating data products onboard the spacecraft adds a new level of flexibility to address the more demanding observation needs. Current spacecraft computing power is limited and incapable of addressing the needs of the new generation of observation satellites because extensive onboard data processing is required. Traditional spacecraft architectures only collect, package, and transmit to Earth the data acquired by multiple instruments. Conversely, the experience on developing ground data systems shows the need for high performance computing systems to process and create information from the instrumentation data. The expectation is that supercomputing technology is required to enable spacecraft to create information onboard. Moving supercomputing capability onboard spacecraft requires an approach that considers an integrated data architecture. Otherwise, it may simply convert a compute-bound problem into a communications bound problem, as has been shown numerous times in the context of massively parallel architectures. What is left to determine are the technologies that will enable spacecraft high performance computing.

Figueiredo, Marco A.↗

NASA NTRS2021

NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis

Using state-of-the-art artificial intelligence (AI)frameworks onboard spacecraft is challenging because common spacecraft processors cannot provide comparable performance to datacenters with server-grade CPUs and GPUs available for terrestrial applications and advanced deep-learning networks. This limitation makes small, lo w-p o we r AI microchip architectures, such as the Google Coral Edge Tensor Processing Unit (TPU), attractive for space missions where the application-specific design enables both high-performance and power-efficient computing for AI applications. To address these challenging considerations for space deployment, this research introduces the design and capabilities of a CubeSat-sized Edge TPU-based co-processor card, known as the SpaceCube Low-power Ed g e Artificial Intelligence Resilient Node (SC-LEARN). This design conforms to NASA’s CubeSat Card Specification (CS2) for integration into next-generation SmallSat and CubeSat systems. This paper describes the overarching architecture and design of the SC-LEARN, as well as, the supporting test card designed for rapid prototyping and evaluation. The SC-LEARN was developed with three operational modes: (1) a high-performance parallel-processing mode,(2)a fault-tolerant mode for onboard resilience, and (3) a power-saving mode with cold spares. Importantly, this research also elaborates on both training and quantization of Tensor Flow models for the SC-LEARN for use onboard with representative, open-source datasets. Lastly, we describe future research plans, including radiation-beam testing and flight demonstration.

Advanced avionics↗

NASA NTRS2018

Digital Technologies at NASA for Science and Engineering

While scientific and engineering advancements used to rely primarily on theoretical studies and physical experiments, today digital technology enabled by petaflops-scale supercomputers is an equal, if not a greater, contributor to such achievements. In addition, computational modeling and simulation serves as a predictive tool that is not otherwise available. As a result, the use of high performance computing is integral to NASA's work in all mission areas such as space exploration, aeronautics, and scientific discovery. But traditional supercomputing alone is not sufficient for all of the space agency's needs. The success of many NASA missions depends on solving complex computing challenges, some of which are NP-hard (decision theory) if using classical solution methods. Quantum computing promises an unprecedented ability to solve such intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. Another disruptive digital technology is neuromorphic computing that uses brain-inspired lessons to generate new architectures that are much more energy efficient, and capable of massive parallel processing and learning in-situ. Finally, with large amounts of observational and computational data sets, the opportunities of big data and data analytics can be leveraged to enable deep learning and knowledge discovery - it's all a massive digital transformation. This talk will be an overview how NASA utilizes digital technologies for its science and engineering efforts.

Biswas, Rupak↗

NASA NTRSDate not supplied

Thermal Protection Materials and Systems at NASA Ames Research Center

Thermal Protection Systems (TPS) are critical for enabling NASA missions involving high-speed atmospheric flight where the entries usually include descending into the atmosphere followed by a trajectory that aims to burn off energy and result in a controlled landing. NASA Ames focuses on qualifying and certifying TPS for current missions, sustaining TPS for future missions, and developing new TPS for upcoming missions where a heritage solution is not viable. More recently there is also a focus on advancing and transferring technologies that can benefit both commercial and government space needs.Developing mature thermal protection systems is a lengthy process involving advanced tools, extensive research, and testing. Design and analysis tools are used to predict aerothermal environments, aid the design of test and flight hardware, and support the testing for the thermal/mechanical response of thermal protection systems. More recently, advances in computational methods help reduce the time and cost of technological advances, aid in optimized material architecture design, and improve material properties and performance. While high-enthalpy testing that simulates the conditions of space flight remains essential for the evaluation and development of TPS materials, computational tools are already showing promise in reducing the need for widespread testing and can help fast-trackthe design cycle.With the exploration of new destinations EDL instrumentation remains an important element of the heatshield and NASA Ames and partners have developed and delivered instrumentation flight hardware in support of recent Mars missions,including Mars Science Lab (MSL) and Mars 2020 (M2020),as well as Artemis Orion. Sensors installed on the heatshield and backshell of spacecraft provide coveted information about the aerodynamic and aerothermal environment during entry.Overthe years NASA Ames has brought several reusable and ablative TPS materials to a level of readiness to hand off to missions and the Thermal Protection Materials branch continues to serve as a TPS steward for the agency.

Thermal protection materials↗

NASA NTRS2019

The Multi-Mission Earth Entry Vehicle for Sample Return Missions – Past, Present, and Future

The Multi-Mission Earth Entry Vehicle (MMEEV) is an enabling technology developed at NASA’s Langley Research Center (LaRC) over the last two decades for returning samples to Earth across a wide array of space science missions. Currently, the MMEEV is being considered for NASA’s Mars Sample Return (MSR) mission. The original vehicle concept, the Earth Entry Vehicle (EEV), was innovated at LaRC in 1998 as a robust solution to return Mars soil samples to Earth under stringent backward contamination requirements. These backward contamination requirements drove the EEV to have higher reliability than any capsule previously designed for a return-to-Earth sample return mission. The EEV achieved this high reliability by employing a passive (no active systems) vehicle architecture optimized for fault tolerance in a compact, low-mass configuration that is extensible to virtually any sample return mission. The original EEV concept utilized a carbon-carbon primary structure with high-density carbon phenolic thermal protection system. The capsule had a 60-degree sphere-cone forebody and a backshell geometry uniquely tailored to produce aerodynamics that would passively re-orient the vehicle if it entered the atmosphere with an off-nominal attitude. Contrary to every other sample return capsule conceived at the time, the EEV was designed to land without a parachute. The vehicle incorporated an integral energy-absorbing crushable structure that protected the Mars sample for landings on surfaces ranging from soft soil to solid concrete. This paper describes 20 years of technological advancements LaRC has incorporated into the EEV architecture to evolve it from the original, MSR-enabling vehicle, to a true multi-mission capability relevant to any sample return mission. The vehicle’s unique Integrated Composite Stiffener Structure (ICoSS) has been optimized for specific strength - supporting high-G atmospheric entries with steep entry angles that produce precise landing footprints on the ground. The vehicle geometry has been refined through wind tunnel testing and computational fluid dynamics simulations to improve the vehicle’s aerodynamic stability and robustness to off-nominal conditions from hypersonic to subsonic flight. The resulting configuration of the current MMEEV architecture is described, with details provided on its sample carrying capacity and atmospheric entry trajectory capabilities. The upgraded vehicle performance is mapped into current space science objectives, showing how the MMEEV supports future sample return missions and continues to be an enabling technology for NASA’s vision to return samples from Mars.

J M Corliss↗