AI for New Space R&D

Supercharge your R&D workflows.

Here are some R&D workflows you can dramatically accelerate with generative AI.

FYI: I’m not affiliated with any of the tools mentioned. I'm also not claiming they're any good — some of them might be rubbish. Others might be gone by the time you read this.

But I figured real examples would be more useful than vague theory.

Let’s dive in.

1. Literature & Patent Review

   AI can summarize research papers and patents to identify relevant findings quickly. For example, you can use an AI tool to distill technical literature on subsystems, standards, and prior missions.

   
   

   Execution Example: Use a tool like Iris AI to summarize a dozen IEEE papers on onboard navigation algorithms.

   
   

2. Concept Validation

   AI simulations can test early ideas against models or data to assess viability. For example, you can use an AI tool to accelerate validation of propulsion or sensor designs using physics-informed ML.

   
   

   Execution Example: Use a tool like NVIDIA PhysicsNeMo to reduce CFD modeling time for a new thruster concept.

   
   

3. Innovation Mining

   AI can find patterns across projects and external sources to suggest novel approaches. For example, you can use an AI tool to uncover overlooked design ideas from space missions or aerospace research.

   
   

   Execution Example: Use a tool like Connected Papers to link your design challenge to novel material use in a Mars rover project..

   
   

4. Test Data Anomoly Detection

   AI can detect deviations in test results that could signal flaws or opportunities. For example, you can use an AI tool to highlight unusual signals from vacuum, thermal, or vibration test logs.

   
   

   Execution Example: Use a tool like Anomoly.io to flag unexpected heat signatures in a vacuum test log.

   
   

5. Experiment Planning

   AI can help design experiments by optimizing test parameters. For example, you can use an AI tool to plan subsystem tests that reduce resource use and improve data quality.

   
   

   Execution Example: Use a tool like Design-Expert to recommend test parameters for a new sensor suite to reduce error variance.

   
   

6. Collaboration Discovery

   AI can identify internal or external collaborators with aligned goals. For example, you can use an AI tool to spot researchers or labs working on similar subsystem or challenges.

   
   

   Execution Example: Use a tool like Lean Library Workspace to suggest a potential partnership with a Canadian lab publishing on similar thermal control issues.

   
   

7. Documentation Automation

   AI can draft test reports or summaries from structured data. For example, you can use an AI tool to create engineering-ready test logs from Jira, Confluence, or telemetry tools.

   
   

   Execution Example: Use a tool like ChatGPT to summarize subsystem test logs into formatted reports in Confluence.

   
   

8. Idea Ranking

   AI can evaluate ideas against strategic and technical criteria. For example, you can use an AI tool to score subsystem concepts by mission value, complexity, and alignment.

   
   

   Execution Example: Use a tool like Notion to score new project proposals against company OKRs and technical fit.

   
   

9. Failure Prediction

   AI can forecast risk of failure based on historical patterns. For example, you can use an AI tool to identify likely failure points in component designs or test conditions.

   
   

   Execution Example: Use a tool like Uptake AI to analyze past component test logs to predict likely failure points in PCB designs.

   
   

10. Grant Opportunity Matching

    AI can identify relevant funding programs based on project keywords. For example, you can use an AI tool to match your mission goals to grants from ESA, DLR, or national programs.

    
    

    Execution Example: Use a tool like Grantable AI to recommend an ESA Pathfinder call based on your current mission focus.