NASA Advanced Computing RFI 2026.pdf

REQUEST FOR INFORMATION (RFI): Enabling Exploration through Partnerships in Advanced Computing

1. PURPOSE

The National Aeronautics and Space Administration (NASA) is issuing this Request for Information (RFI) to gather information from the community on potential approaches and capabilities that may inform future partnership strategies with American industry and academic institutions to address advanced computing gaps that are critical to NASA’s mission. NASA seeks information on capabilities, technologies, and collaboration approaches across the three complementary theme areas described herein, with the goal of addressing critical mission challenges while advancing U.S. leadership in advanced computing and artificial intelligence

(AI).

NASA is not seeking proposals for any specific procurement or non‑procurement activity at this time. Responses will help NASA understand the landscape of potential approaches. If NASA elects to proceed with a future competitive process, the use of any particular acquisition or partnership mechanism (e.g., contracts, grants, cooperative agreements, or partnership agreements) will be defined in any future announcements.

The Science Mission Directorate (SMD) is leading this RFI development and assessment and will coordinate with the Exploration Systems Development Mission Directorate, Aeronautics Research Mission Directorate, Space Technology Mission Directorate and Space Operations Mission Directorate in its assessment of RFI responses.

2. DISCLAIMER

This RFI is issued solely for information-gathering and planning purposes only. It does not constitute a solicitation or a commitment by the Government to procure any services. This is not a request for proposal or quotation, nor is this a solicitation for a contract or grant award. This RFI does not obligate the Government in any way. The Government will not reimburse the respondents for any costs associated with the information submitted in response to this request. No corresponding solicitation exists; therefore, do not request a copy of the solicitation.

3. BACKGROUND

NASA’s next generation of missions, whether probing the mysteries of the universe, advancing aeronautics, or enabling sustained human presence on the Moon and Mars, will depend on the ability to harness advanced computing and AI at unprecedented scales. These capabilities are essential to transforming how NASA designs, certifies, and operates complex systems in environments where autonomy, efficiency, and resilience are critical. Furthermore, advanced computing and AI capabilities are core infrastructure that are essential to maintaining U.S.

leadership in space exploration, science, and aeronautics.

Significant computational constraints currently limit NASA’s progress. NASA’s existing high-end computing resources cannot keep pace with the growing demand for large-scale simulations, integrated digital environments, and AI-driven workflows. Reliance on Central Processing Unit (CPU)-centric architectures and fragmented modernization efforts limit the effective use of Graphics Processing Unit (GPU) acceleration and heterogeneous computing architectures. For long duration and deep space missions, the challenge is even greater: legacy radiation-hardened (rad-hard) hardware limits onboard processing and autonomy, forcing heavy reliance on Earth-based teams and slowing decision making in environments where real-time responses are vital.

Partnering with American industry offers NASA a transformative opportunity to overcome current computing barriers. Industry has developed powerful hyperscale platforms and advanced engineering ecosystems, but these systems are not yet tailored to NASA’s mission profiles or the extreme environments in which the agency operates. Combining NASA’s unique mission context, datasets, and expertise with industry’s innovation and scale can extend computing to new frontiers and enable breakthroughs that accelerate progress across NASA’s missions. These collaborations can also strengthen the U.S. industrial base in advanced computing and AI, reinforcing strategic leadership in space and technology.

In parallel, academic institutions can partner with NASA on advanced computing initiatives by contributing foundational research, novel architectures, and interdisciplinary talent. Academia’s experimental capabilities and technical expertise can translate cutting edge ideas into mission-ready solutions while fortifying the national talent pipeline required for sustained U.S. leadership in advanced computing and exploration.

4. SCOPE – AREAS OF INTEREST

NASA is seeking input from industry and academic institutions on three complementary themes that address critical computing challenges across its missions. These themes directly support the nation’s need for resilient, high performance computing capabilities that underpin U.S.

leadership in space:

Theme 1: Adaptive Mission Intelligence Platforms will enable integrated digital environments that fuse diverse datasets with advanced modeling and AI to improve mission planning, certification, and real-time decision making.

Theme 2: Computing in Extreme Environments targets the development of rad-hard, AI-enabled edge systems for autonomous operations in lunar, Martian, Sun-Earth Lagrange point 2 (L2), and other deep space conditions, reducing reliance on Earth-based support.

Theme 3: Advanced High-End Computing for Integrated Aerospace Systems seeks to scale simulation and uncertainty quantification to mission-level fidelity, leveraging heterogeneous architectures and physics-informed AI to accelerate design, analysis, and certification for next-generation aerospace systems.

The section ‘Detailed Theme Descriptions’ provides more information on each of these areas.

NASA seeks information that may inform potential future collaboration models or mechanisms

(procurement or non‑procurement) that address critical mission challenges while advancing U.S.

leadership in advanced computing and AI. The agency is exploring collaborations in four key areas:

secure, high performance on-premises systems for sensitive workloads;

episodic surge access to commercial platforms for large-scale simulations and AI model training;

resilient, AI-enabled computing for space to support autonomous, Earth-independent operations; and access to next-generation computing systems within partner facilities for testing and evaluation.

Future partnerships should feature direct, hands-on collaboration between NASA and external teams, enabling the exchange of expertise through joint technical efforts. These teams will work together to modernize legacy software, optimize data processing, and customize computing systems for NASA’s mission needs. This collaborative approach fosters innovation while ensuring mission assurance, security, and accountability.

This initiative lays the foundation for a new era of exploration, science, and aeronautics by pairing NASA’s mission challenges with industry’s most advanced computing capabilities and academia’s unique expertise and talent. Together, these collaborations will enable breakthroughs in simulation, AI-driven decision making, and resilient systems that push the boundaries of human presence in space and transform flight and discovery on Earth.

Any future activity informed by this RFI may proceed through a procurement or non‑procurement mechanism, consistent with applicable NASA policy and statutory authorities.

NASA is not committing to any specific mechanism, structure, or competitive approach.

5. REQUESTED INFORMATION

NASA requests that interested parties include the following sections and information in their responses. Respondents need not address every item but should provide sufficient detail to enable NASA to assess the potential for mutually beneficial collaboration.

Respondent Information

Legal entity name, UEI/CAGE, primary place of performance, socio-economic status, and suggested NAICS code for this effort.

Primary point of contact for response (name, title, phone, email).

Brief description of the organization's mission, core competencies, and relevant experience delivering high-end computing capabilities to U.S. federal agencies.

Theme Alignment and Technical Capabilities

Clearly identify which of the three themes identified in this RFI are being addressed.

Respondents may address one, two, or all three themes.

Current or planned capabilities relevant to the identified theme(s), including hardware, software, computing architectures, artificial intelligence/machine learning (AI/ML) frameworks, or integrated systems.

How the respondent’s capabilities address one or more of NASA's identified computing gaps or mission challenges.

Relevant domain experience (e.g., high-performance computing (HPC), edge computing in extreme environments, large-scale simulation, AI-enabled autonomy, heterogeneous architectures, environmental testing and qualification).

What distinguishes the respondent's capabilities from commercially available alternatives.

Partnership Approach

Potential collaboration goals, including any in-kind or cost-sharing contributions.

Summary of partnership scope, objectives, and expected outcomes.

Approach to embedded engineering collaboration with NASA, such as knowledge transfer, code modernization, or architecture alignment.

Technical Maturity and Readiness

Current Technology Readiness Level (TRL) or equivalent maturity of the respondent’s capabilities.

Examples or references demonstrating prior performance in relevant application areas.

How the capabilities can scale and adapt across NASA mission profiles or operational environments.

Security, Compliance, and Mission Assurance

Ability to meet NASA security requirements, including FISMA standards and cybersecurity frameworks.

Experience supporting ITAR, EAR, PHI, PII, or other regulatory compliance within computing environments.

Approaches to ensuring mission assurance, accountability, and traceability.

Workforce and Talent Development (if applicable)

Opportunity for workforce development or talent pipeline initiatives (e.g., internships or student engagement) resulting from the collaboration that would strengthen national talent for sustained leadership in advanced computing and exploration.

6. RESPONSE INSTRUCTIONS

All responses must be received by 11:59 p.m. Eastern time on June 1, 2026. Responses must be submitted electronically via email to HQ-HEC-Partnerships@mail.nasa.gov.

Responses should not exceed 10 pages total (excluding cover page and optional appendices), 12-point font, 1-inch margins. Respondents may address one, two, or all three themes within the 10-page limit. Responses must be submitted as a PDF-formatted document that follows the naming convention “OrganizationName_Advanced-Computing-RFI-Response.pdf”.

Responses should not include any classified or ITAR/EAR-restricted information.

Proprietary or confidential information should be clearly marked and will be protected to the extent permitted by law. To the full extent that it is protected pursuant to the Freedom of Information Act and other laws and regulations, information identified by a respondent as "Proprietary or Confidential" will be kept confidential.

Teaming arrangements are permitted and should be described in the response.

7. POINTS OF CONTACT

Questions regarding this RFI may be directed to HQ-HEC-Partnerships@mail.nasa.gov.

The following officials are overseeing this RFI and the assessment of any responses and can be reached using the email address HQ-HEC-Partnerships@mail.nasa.gov.

Rachel Paseka Office of the Chief Science Data Officer Science Mission Directorate National Aeronautics and Space Administration

Kevin Murphy Office of the Chief Science Data Officer Science Mission Directorate National Aeronautics and Space Administration

8. DETAILED THEME DESCRIPTIONS

Theme 1: Adaptive Mission Intelligence Platforms

Opportunities for NASA

NASA is exploring opportunities to develop adaptive digital environments to derive actionable knowledge from diverse datasets spanning science, exploration, and aeronautics, making use of NASA’s vast data archives and data from new missions. This will require the integration of diverse data sets at volumes and speeds that are currently unattainable in both experimental and operational contexts. To do so, NASA seeks opportunities to create digital environments with sufficient computational power to integrate large volumes of data and create high-fidelity simulations that leverage AI and other advanced modeling techniques.

These digital environments have the potential to improve success for long duration space missions, reduce mission risk, accelerate mission planning, and enhance real-time situational awareness to inform human decision making and autonomous operations.

Opportunities include:

Transforming astronaut health and risk modeling by creating systems that integrate biomedical, physiological, and AI models with mission and environmental data. These platforms will enable predictive health analytics, early risk detection, and personalized care to improve safety and resilience for long-duration missions.

Preparing for sustained human exploration of the Moon and Mars by developing digital environments that integrate vast scientific datasets with physics-based and AI models.

These environments will support planning for future missions and provide real‑time scientific insights for mission operations. Together, these capabilities will enhance decision making for control center teams and astronauts, while also strengthening guidance and navigation capabilities for autonomous spacecraft.

Advancing airspace and space traffic operations through high fidelity digital twins that integrate aeronautics and trajectory data with AI-driven workflows. These platforms will optimize complex scenarios, enhance safety, and improve efficiency across aviation and space domains.

Accelerating mission certification and flight readiness by building secure, automated platforms that harmonize thousands of engineering and programmatic datasets. These systems will streamline decision making, strengthen risk assessment, and reduce labor-intensive processes to enable faster, more reliable certification for Moon to Mars missions.

NASA’s Advanced Computing Gaps

NASA faces significant limitations in compute capacity, architectural flexibility, and operational requirements that must be addressed to enable advanced mission capabilities. An order-of-magnitude increase in effective compute capability is essential to support the integration of heterogeneous datasets at scale and enable advanced modeling and simulation. This expansion must be paired with multi-tiered storage solutions that span active processing through long-term archival needs. NASA’s current reliance on CPU-centric solvers and limited GPU-optimized libraries constrain performance. NASA requires GPU-enabled, multi-threaded architectures and the ability to test across industry-relevant platforms to ensure compatibility and scalability.

These systems should include an integrated performance infrastructure with high model fidelity and the ability to incorporate extended reality (XR) for real-time operations. These systems must include safeguards for secure and critical workloads, including Personal Health Information (PHI), Personally Identifiable Information (PII), Export Administration Regulations (EAR), and International Traffic in Arms Regulations (ITAR) compliance. Future systems must deliver self-contained, autonomous solutions capable of sustained, Earth-independent operations during long-duration missions.

Opportunities for Partners

Future collaboration mechanisms, if pursued, may provide partners with access to NASA’s unique mission context, datasets, and expertise, focusing on some of the agency’s most challenging data integration and modeling problems spanning science, exploration, and aeronautics. Future partnerships may include the development of high-fidelity digital environments that enhance readiness for Earth-independent and autonomous systems, such as AI-driven platforms for exploration, aviation, and personalized health. Additionally, future partnerships may pilot secure and resilient systems tailored for extreme environments, showcasing autonomous capabilities for astronaut health modeling and long-duration missions.

Any potential future activity informed by this RFI may be executed through the appropriate NASA mechanism, whether procurement or non‑procurement, determined at a later stage in accordance with Agency policy.

Theme 2: Computing in Extreme Environments: AI-Enabled Edge Computing for Moon to Mars Operations

Opportunities for NASA

NASA faces critical challenges in enabling autonomous operations and HPC in extreme lunar, Martian, and other deep space environments. The state of the art in modern computing is insufficient to meet the needs of long-duration missions in these extreme environments.

Partnerships offer a transformative opportunity to accelerate the adoption of advanced microelectronics, rad-hard architectures, and edge AI capabilities, reducing reliance on Earth-based teams and improving mission resilience. By collaborating with industry on testing, validation, and deployment of next-generation computing platforms, NASA can close technology gaps that currently limit autonomy and advanced analytics in deep-space missions, accelerating the integration of flight-qualified systems to meet critical timelines. Opportunities include:

Developing rad-hard, AI-capable processors and fault-tolerant software to enable autonomous robotic exploration and real-time decision making for Moon to Mars operations and servicing scientific platforms at Sun-Earth L2. Replacing legacy architectures (e.g., RAD-750) with high performance, fault-tolerant systems will support real-time data processing and mission resilience.

Validating modern compute architectures for extreme environments, including radiation, thermal, vacuum, and dust tolerance, to support avionics systems capable of high performance processing in harsh conditions.

Implementing new and emerging semiconductor technologies and packaging solutions for space-qualified systems, addressing supply chain constraints and performance limitations of legacy hardware.

Accelerating NASA’s software certification process for edge computing systems using explainable AI (XAI) models. XAI will enable NASA to update its software certification processes to increase decision velocity while supporting a resilient edge computing ecosystem.

NASA’s Advanced Computing Gaps

NASA’s computing landscape remains constrained by legacy flight hardware, limited qualification of modern architectures, and the absence of a robust edge AI ecosystem. Continued dependence on RAD750–class and other heritage rad-hard electronics restricts real-time onboard processing, advanced autonomy, and AI enabled decision making essential for sustained Moon to Mars operations. These challenges are amplified by strict mass limits and long communication delays for future missions that would prevent spacecraft from relying on Earth-based high performance computing for time critical AI tasks. Hardware limitations are compounded by insufficient radiation and environmental test data for high performance GPUs and modern microelectronics, slowing adoption and forcing conservative design choices that often require additional shielding and reduce overall mission capability.

At the same time, NASA lacks an integrated, fault-tolerant edge computing environment that combines processors, memory, storage, and power systems designed for extreme conditions.

Without such an ecosystem, deploying high bandwidth instrument pipelines, AI workloads, and resilient autonomous systems remains difficult across lunar, Martian, and deep space missions.

Finally, NASA’s software certification processes cannot accommodate opaque AI models or Large Language Model (LLM)-based capabilities. To safely incorporate modern AI into mission critical systems, NASA requires explainable AI frameworks and certification ready edge computing architectures built on contemporary software libraries and XAI methodologies.

Opportunities for Partners

Future collaboration mechanisms, if pursued, may provide partners with access to NASA’s unique mission context, datasets, and deep expertise in extreme environments, converting insights into radiation, dust, thermal, and vacuum challenges into robust design choices for advanced computing systems. Future partnerships may co-develop edge AI and autonomous solutions grounded in mission driven use cases, refining AI-capable processors, fault tolerant software, and integrated edge ecosystems engineered for harsh operational conditions. These engagements may also create a direct pathway to integration with high profile Moon to Mars missions, enabling flight qualification and deployment while offering companies the opportunity to showcase innovation in globally visible programs.

Any potential future activity informed by this RFI may be executed through the appropriate

Theme 3: Advanced High-End Computing for Integrated Aerospace Systems

Opportunities for NASA

NASA’s aerospace missions increasingly depend on high-fidelity computational fluid dynamics (CFD), uncertainty quantification (UQ), and emerging AI/ML integration to support design, analysis, and certification. While medium-scale simulations (100–500 million cells) are routinely used to inform design optimization, scaling these capabilities to mission-level, multi-disciplinary configurations with ubiquitous uncertainty quantification requires an order-of-magnitude increase in effective compute capability.

The strategic objective is to deliver an integrated, adaptive-fidelity simulation ecosystem that tightly couples computation with validated experimental and flight datasets. Central to this effort is building a comprehensive computing strategy that leverages the right mix of CPU, GPU, and emerging Quantum Processing Unit (QPU) resources optimized for the specific physics and fidelity requirements of each problem class. Achieving mission level, uncertainty- aware simulation requires not only greater compute throughput but also deeper integration across CFD, UQ, multi-physics solvers, data management, and AI workflows—areas where current toolchains remain fragmented. This approach would enable routine, large-scale UQ campaigns, full-vehicle multi-physics simulation, and the generation of physics-based datasets to support AI/ML development and reduced-order modeling.

Opportunities include:

Advancing high‑fidelity Entry, Descent, and Landing (EDL) modeling and overall flight design through GPU‑accelerated multi-physics solvers, AI‑driven surrogate models, and surge commercial HPC, enabling Monte Carlo CFD‑in‑the‑loop analyses that shorten design cycles and improve predictive capability for simulating Moon to Mars landings.

Enabling fully coupled manifold–injector–combustor simulations for rotating detonation rocket engines (RDRE) using GPU‑optimized multi‑physics solvers and surge commercial HPC to cut months‑long runtimes, incorporate upstream feed‑system dynamics, and support rapid iterative design of higher‑performance, stable propulsion systems for Moon to Mars missions.

Improving high‑fidelity Supercooled Large Droplet (SLD) icing prediction through GPU‑enabled multiphase, unsteady CFD and tighter coupling between icing physics and aero‑performance models, delivering certification‑grade analyses, more accurate ice‑shape evolution, and expanded parametric envelopes for safer aircraft operations.

Advancing propulsion–airframe integration by GPU‑scaling unsteady, rotating‑frame CFD and multi‑physics coupling across aero, acoustics, structures, icing, and materials, enabling high‑fidelity rotor‑wake interaction modeling, improved noise and fatigue prediction, and reduced certification risk for emerging open‑rotor concepts.

NASA’s Advanced Computing Gaps

While NASA has strong disciplinary expertise and well-established CFD workflows, it faces constraints in both computing capacity and solver capability that limit progress toward mission scale, uncertainty-aware simulation. Limitations in compute capacity hinder 100–500M‑cell simulations that require turnaround times currently incompatible with iterative design. Uneven progress in GPU modernization across the agency hinders NASA’s ability to perform routine ensemble‑scale UQ or generate the large, validated datasets needed for physics‑informed AI.

These capacity limitations are compounded by capability gaps, including continued reliance on CPU-centric solvers with limited GPU-optimized libraries, fragmented GPU modernization efforts across organizations, and data management, visualization, and analysis practices that have not kept pace with expanding simulation scales. Together, these limitations slow the transition from deterministic analyses toward confidence-based engineering decisions required for certification-relevant, safety-critical systems.

These gaps can be addressed through the development of an adaptive‑fidelity ecosystem where heterogeneous compute, portable multi‑physics solvers, and automated UQ pipelines enable certification grade decision making at timescales that are meaningful for mission design.

Opportunities for Partners

Future collaboration mechanisms, if pursued, may provide partners with the opportunity to collaborate on GPU‑optimized solvers, portable software stacks, and automated UQ workflows using NASA’s mission‑validated datasets from wind‑tunnel and flight tests. Collaboration with NASA may accelerate GPU modernization, solver optimization, and development of portable software stacks and advanced visualization frameworks. Application areas such as EDL, propulsion–airframe integration, multiphase icing, and extreme structural environments may expose future partners to complex multi-physics challenges validated through wind tunnel and flight testing. These mission-driven opportunities can help mature physics-informed AI and computing technologies that translate directly into robotics, autonomy, and other safety critical systems.

Any potential future activity informed by this RFI may be executed through the appropriate