A MBSE Approach to MDAO Systems for the Development of Complex Products

The research and innovation AGILE project has developed an approach, the so-called AGILE Paradigm, focusing on the acceleration of the deployment and operation of collaborative Multidisciplinary Design Analysis Optimization systems, which in turns can be exploited to accelerate the development of complex products, such as novel aerospace systems. Although the technologies developed for the implementation of the paradigm, have proved to reduce the deployment and operational time to more than 40% with respect to conventional MDAO approaches, the AGILE Paradigm has not been formalized and model by digital design engineering practices. This work introduces a novel approach leveraging MBSE principles to streamline the development of agile MDAO design systems, and establishing a bridge between MBSE and MDAO. Major outcomes here presented are the MBSE-driven models of the so-called AGILE MDAO system, representing the architecture, the requirements, as well as the organizational aspects, and all the interactions and activities implemented during the life-cycle stages of the MDAO system. The MBSE Architectural Framework, which defines the underlying ontological concepts and perspectives driving the development of the AGILE MDAO system model, are modeled and presented as well. The paper introduces for the first time the overall approach, as well as the high-level elements of the models developed, here represented by making use of SysML standard. The described approach is at the core of the recently launched project AGILE4.0, in which its scope will be expanded to cover the entire life-cycle of the development of complex aeronautical systems

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon †

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines