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Large Language Models (LLMs) have demonstrated remarkable reasoning abilities, prompting interest in their application as black-box optimizers. This paper asserts that LLMs possess the capability for zero-shot optimization across diverse scenarios, including multi-objective and high-dimensional problems. We introduce a novel population-based method for numerical optimization using LLMs called Language-Model-Based Evolutionary Optimizer (LEO). Our hypothesis is supported through numerical examples, spanning benchmark and industrial engineering problems such as supersonic nozzle shape optimization, heat transfer, and windfarm layout optimization. We compare our method to several gradient-based and gradient-free optimization approaches. While LLMs yield comparable results to state-of-the-art methods, their imaginative nature and propensity to hallucinate demand careful handling. We provide practical guidelines for obtaining reliable answers from LLMs and discuss method limitations and potential research directions.

The use of Digital Twins is set to transform the manufacturing sector by aiding monitoring and real-time decision making. For several applications in this sector, the system to be modeled consists of a mix of discrete-event and continuous processes interacting with each other. Building simulation-based Digital Twins of such systems necessitates an open, flexible simulation framework which can support easy modeling and fast simulation of both continuous and discrete-event components, and their interactions. In this paper, we present an outline and key design aspects of a Python-based framework for performing mixed discrete-continuous simulations. The continuous processes in the system are assumed to be loosely coupled to other components via pre-defined events. For example, a continuous state variable crossing a threshold may trigger an external event. Similarly, external events may lead to a sudden change in the trajectory, state value or boundary conditions in a continuous process. We first present a systematic events-based interface using which such interactions can be modeled and simulated. We then discuss implementation details of the framework along with a detailed example. In our implementation, the advancement of time is controlled and performed using the event-stepped engine of SimPy (a popular discrete-event simulation library in Python). The continuous processes are modelled using existing frameworks with a Python wrapper providing the events interface. We discuss possible improvements to the time advancement scheme, a roadmap and use cases for the framework., Comment: Under review for publication in Springer Lecture Notes in Networks and Systems (LNNS)