Multiscale Structure

Multiscale structure research focuses on designing and optimizing materials and systems with properties varying across multiple length scales, aiming to achieve superior performance compared to single-scale designs. Current efforts leverage machine learning, particularly neural networks, for efficient inverse homogenization and topology optimization, often incorporating constraints based on physical principles like hyperelasticity or topological data analysis to ensure model fidelity and robustness. These advancements are impacting diverse fields, enabling the creation of high-performance materials with tailored microstructures for additive manufacturing and improving the efficiency and reliability of deep learning algorithms through data-informed optimization strategies.