Generative AI for the Design of Complex Systems: Roadmap and Space Mission Case Study
R. M. García Alarcia and A. Golkar
Complex Adaptive Systems Conference (CAS 2025), MIT, Boston; published in Procedia Computer Science, vol. 268, pp. 348–356, Elsevier, 2025
Abstract
This paper sets the foundation for a roadmap aimed at improving the efficiency of designing complex systems by leveraging generative AI. Our focus is on integrating generative AI, specifically LLMs, with semantic system models and computation engines to improve system design capabilities. We introduce a prototype assistant tool for the design of space systems, validate it and demonstrate an improvement in the efficiency of the design process with a comprehensive case study, that includes the design of Earth Observation missions benchmarked to the European Sentinel infrastructure and the EventSat mission we are developing. The tool generates, among others, typical design artifacts such as System Requirements Specifications, Product Breakdown Structures, and Interface Control Documents. We validate it through the cosine similarity of the produced text to the ground truth, a design structure matrix to compare system dependencies and strength and manual evaluation with experts. Validation results align closely with the ground truth. Design time and associated development costs are significantly reduced when using the tool, promising an improvement in efficiency. We contextualize our work in the broader context of developing generative AI for the design of complex systems, by presenting a technology roadmap with our vision of future developments. We discuss the potential of incorporating generative AI into the design of complex systems, the status of technology and the necessary developments. Generative AI integration will enable efficient complexity management, paving the way for innovative advancements in systems engineering.
Keywords: generative artificial intelligence, complex adaptive systems, space systems, engineering design
Cite
@article{GarciaAlarcia2025GenerativeAI,
author = {Garc{\'\i}a Alarcia, Ram{\'o}n Mar{\'\i}a and Golkar, Alessandro},
title = {Generative {AI} for the Design of Complex Systems: Roadmap and Space Mission Case Study},
journal = {Procedia Computer Science},
volume = {268},
pages = {348--356},
publisher = {Elsevier},
note = {Complex Adaptive Systems Conference 2025 (CAS 2025)},
year = {2025},
doi = {10.1016/j.procs.2025.08.213},
url = {https://mediatum.ub.tum.de/1833829},
abstract = {This paper sets the foundation for a roadmap aimed at improving the efficiency of designing complex systems by leveraging generative AI. Our focus is on integrating generative AI, specifically LLMs, with semantic system models and computation engines to improve system design capabilities. We introduce a prototype assistant tool for the design of space systems, validate it and demonstrate an improvement in the efficiency of the design process with a comprehensive case study, that includes the design of Earth Observation missions benchmarked to the European Sentinel infrastructure and the EventSat mission we are developing. The tool generates, among others, typical design artifacts such as System Requirements Specifications, Product Breakdown Structures, and Interface Control Documents. We validate it through the cosine similarity of the produced text to the ground truth, a design structure matrix to compare system dependencies and strength and manual evaluation with experts. Validation results align closely with the ground truth. Design time and associated development costs are significantly reduced when using the tool, promising an improvement in efficiency. We contextualize our work in the broader context of developing generative AI for the design of complex systems, by presenting a technology roadmap with our vision of future developments. We discuss the potential of incorporating generative AI into the design of complex systems, the status of technology and the necessary developments. Generative AI integration will enable efficient complexity management, paving the way for innovative advancements in systems engineering.},
keywords = {generative artificial intelligence, complex adaptive systems, space systems, engineering design},
file = {2025/2025_GarciaAlarcia_GenerativeAIDesignComplexSystems.pdf}
}