Over the past few decades, modeling, simulation, and optimization tools have received
attention for their ability to represent and improve complex systems. The use of …

Initialization of metaheuristics is a crucial topic that lacks a comprehensive and systematic
review of the state of the art. Providing such a review requires in‐depth study and …

We use a physics-informed neural network (PINN) to simultaneously model and optimize the
flow around an airfoil to maximize its lift to drag ratio. The parameters of the airfoil shape are …

The application of white box models in digital twins is often hindered by missing knowledge,
uncertain information and computational difficulties. Our aim was to overview the difficulties …

Abstract Image-Based Simulation (IBSim) is the process by which a digital representation of
a real geometry is generated from image data for the purpose of performing a simulation …

Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the
development of Large Language Models (LLMs). While much of the current research in this …

Most optimization problems in engineering can be formulated as 'expensive'black box
problems whose solutions are limited by the number of function evaluations. Frequently …

Abstract Machine Learning (ML) techniques have been used in an extensive range of
applications in the field of structural and multidisciplinary optimization over the last few …

We describe a method for precisely regulating the gradient magnet power supply (GMPS) at
the Fermilab Booster accelerator complex using a neural network trained via reinforcement …

Simulation-based optimization using surrogate models enables decision-making through
the exchange of data from high-fidelity models and development of approximations. Many …