Model Reduction

Model reduction aims to create simplified, lower-dimensional representations of complex systems, improving computational efficiency without sacrificing significant accuracy. Current research emphasizes data-driven approaches, employing techniques like autoencoders, Koopman theory, and neural networks (including recurrent and convolutional architectures) to learn reduced-order models from high-fidelity data. These advancements are impacting diverse fields, enabling real-time control of systems like soft robots and accelerating simulations of turbulent flows and partial differential equations, ultimately enhancing scientific understanding and technological applications.