Designing space missions, with language models.

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

The design of spacecraft, and more broadly of space missions, can greatly benefit from LLM-based design assistants supporting engineers in the extraction of relevant information from past missions for its reuse in the new one, as well as in the automatic generation of system parameters or requirements in text at different levels. However, due to the mathematical nature of technical systems, many of the spacecraft parameters or requirements are numerical and involve calculations or the application of first principles. While Large Language Models excel at text generation, predicting the next words in a sequence with the highest probability, they are not suited, being statistical engines, to perform deterministic computations. In this paper, we present the architecture of a computation engine to which numerical calculations are outsourced during the process of generating parameters or requirements by an LLM-based design assistant. We present the integration with the LLM, measure its performance, and benchmark and discuss the improvement in parameters generation with a space mission as a use case. In particular, the computation engine hereby developed follows an object-oriented programming paradigm, mimicking through the classes the subsystems of a spacecraft, through the class properties their parameters, and through the class functions the equations and first principles used to calculate the parameters' values, which can be then expressed by the LLM as numerical system parameters or requirements with natural language. A high-level programming language and a modular approach are followed in seek of high readability and modifiability, allowing the engine to be extended and its granularity increased in future iterations. Different alternatives for the integration of the computation engine with the LLM and in the design flow are evaluated and compared, and parameters such as the computation speed and error are measured and assessed along different computation platforms. By presenting this computation engine for spacecraft design, we aim at patching the shortcomings of LLM-based design assistants in numerical calculations, paving the way for their adoption and thus helping accelerate and simplify spacecraft design tasks for a broader range of institutions and individuals.Keywords: spacecraft design assistant, computation engine, numerical system parameters, Large Language Model, Generative AI