Order Model

Order models encompass a broad range of techniques aiming to simplify complex systems by reducing their dimensionality or complexity, thereby enabling faster and more efficient simulations or analyses. Current research focuses on developing and improving these models, particularly through the use of domain decomposition methods (like the Schwarz alternating method), deep learning architectures (e.g., autoencoders, DeepONets, and recurrent neural networks), and operator inference. These advancements are significantly impacting various fields, including fluid dynamics, material science, and supply chain optimization, by providing more accurate and computationally tractable representations of intricate systems.