AI use-case explorer
AI use cases
Search and filter every reported use case. Open a record for the agency's full description, governance answers and source provenance.
Columns (7)
| Use case | Agency | Stage | High-impact | Topic area | AI classification | Sourcing |
|---|---|---|---|---|---|---|
| Instantaneous Clarity of Ambient eNvironment Capability (ICAN-C) NASA-445 · MSFC: Marshall Space Flight Center | NASA | Pilot | Not high-impact | Science | Generative AI | Developed in-house |
| AI Digital Assistants (HRP) [was "Doc in a Box" for Earth Independent Medical Operations (EIMO)] NASA-487 · JSC: Johnson Space Center | NASA | Pilot | Presumed high-impact, determined not | Health and Medical | Agentic AI | Developed in-house |
| Data Fracking (HRP) NASA-488 · JSC: Johnson Space Center | NASA | Pilot | Not high-impact | Science | Natural language processing | Developed in-house |
| Design Optimization of Turbomachinery Rotor Blades using Neural Network Surrogate Models NASA-526 · GRC: Glenn Research Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| ChatGSFC NASA-539 · GSFC: Goddard Space Flight Center | NASA | Pilot | Not high-impact | Science | Generative AI | Contract and in-house |
| Implement machine learning in NMIS to assist with mishap coding NASA-545 · OSMA: Office of the Chief Safety & Mission Assurance | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| GenAI Agent for Interacting with Robots (https://github.com/nasa-jpl/rosa) NASA-709 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Science | Generative AI | Developed in-house |
| SLIM: Software Lifecycle Improvement & Modernization NASA-711 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Information Technology | Generative AI | Developed in-house |
| Using LLMS for Documents and Requirements Analysis NASA-712 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Administrative Functions | Natural language processing | Developed in-house |
| Adversarial Policy Evolution via AI NASA-808 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Science | Reinforcement learning | Developed in-house |
| Adaptive Problem Solving / Hyperparameter Optimization NASA-809 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Time Series Forecasting, Evaluation and Deployment (Time-FED) NASA-814 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| AI Risk-aware task and motion planning for snake-like robots in icy enviroments NASA-815 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Retrieving stratospheric NO2 profiles from OMPS Limb Profiler measurements NASA-850 · GSFC: Goddard Space Flight Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Quantification of Uncertainty Analysis Toolkit (QUAnT) NASA-861 · GSFC: Goddard Space Flight Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| Cloud Account Allocation Plan (CAAP) Cost Analytics Support NASA-901 · GSFC: Goddard Space Flight Center | NASA | Pilot | Not high-impact | Administrative Functions | Classical / predictive ML | Contract and in-house |
| Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model NASA-13 · ARC: Ames Research Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| AI-enabled Risk-Informed Assessment Selection (RIAS) tool NASA-746 · OSMA: Office of the Chief Safety & Mission Assurance | NASA | Pilot | Not high-impact | Other | Generative AI | Developed in-house |
| AI Based Digital Twin Interoporability Schema NASA-798 · JPL: Jet Propulsion Laboratory | NASA | Pilot | Not high-impact | Science | Reinforcement learning | Developed in-house |
| Machine learning for prediction and factor analysis of laser forming parameters NASA-932 · MSFC: Marshall Space Flight Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
| RDUST Regolith Dust Universal System Tracker NASA-935 · MSFC: Marshall Space Flight Center | NASA | Pilot | Not high-impact | Science | Classical / predictive ML | Developed in-house |
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