AI for Rapid Development of Space Precision Components

NASA's Text-to-Spaceship Vision has highlighted the potential for AI-enabled, agentic design automation to accelerate space hardware development and improve performance through rapid iteration and broader trade-space exploration. Future astrophysics missions require space precision components with extreme stability, alignment control, low noise, and high reliability across thermal, structural, optical, cryogenic, and electronics domains. These components are often custom, tightly coupled across disciplines, and developed through long, iteration-heavy cycles that drive schedule and cost risk. NASA seeks innovative AI-enabled design automation approaches that can reduce the time from requirements to manufacturable, verified designs for instrument and observatory components by at least 5 , while improving performance, traceability, and manufacturability. This subtopic solicits AI-based design automation tools or frameworks that ingest concise requirements (natural language, MBSE artifacts, or standard digital files such as STEP and IGES), generate a candidate design, execute embedded physics-based or surrogate analyses, and return a verification report with pass/fail metrics traceable to requirements. Approaches may use agentic workflows and, where multiple disciplines are coupled, multi-agent workflows in which specialized agents collaborate across domains such as structures, thermal, optics, electronics, and manufacturing to automate handoffs and converge on validated designs with human engineers in the loop for expert review. Solutions should be applicable to one or more precision component classes relevant to astrophysics instruments and observatories, including but not limited to optomechanical mounts and benches, thermal control hardware, detector packaging and readout integration, precision mechanisms, cryogenic interfaces, structures and materials for stability, optical elements and optical system layouts, and manufacturing process planning for tight tolerances. Proposed solutions must focus on hardware design acceleration and, in Phase I, demonstrate feasibility on at least one subsystem or component relevant to astrophysics mission needs. Preference will be given to approaches that: (1) demonstrate an end-to-end automated design loop completed at least 5 faster than current practice, (2) incorporate embedded analyses with requirement-traceable metrics, (3) provide real-time manufacturability feedback for relevant techniques including CNC machining, additive manufacturing, PCB fabrication, composites, and optics fabrication using open standards (STEP/STEP-NC, IPC-2581, FMI, REST/JSON, and optical prescription and wavefront representations), and (4) use an agentic or plug-in architecture that can add capabilities for additional subsystems in Phase II. Note that generative AI specifically for instrument optical hardware technologies should be directed to COSMO.4.S26B while this subtopic is for AI-based frameworks.