Benchmark Multiscale System

Benchmark multiscale systems research focuses on efficiently modeling and simulating systems exhibiting vastly different scales of behavior, a challenge across numerous scientific domains. Current efforts concentrate on developing improved multiscale algorithms, including those leveraging radial basis functions, multirate gradient descent, and deep learning architectures like deep recurrent neural networks and physics-embedded machine learning models. These advancements aim to enhance accuracy, interpretability, and computational efficiency in analyzing complex systems, with applications ranging from material science (e.g., composite materials) to machine learning (e.g., federated learning with concept drift). The ultimate goal is to create robust and scalable methods for tackling high-dimensional, multiscale problems that are currently intractable using traditional approaches.