Heterogeneous Material

Heterogeneous materials, characterized by spatially varying properties, pose significant challenges for accurate modeling and prediction of their mechanical behavior. Current research focuses on developing data-driven surrogate models, employing neural networks such as UNets, Fourier Neural Operators (FNOs), and Long Short-Term Memory (LSTM) networks, often incorporating physics-informed constraints to improve accuracy and efficiency. These advancements enable faster and more accurate simulations, particularly for complex scenarios like polycrystalline materials and biological tissues, facilitating improved design and analysis in diverse engineering and scientific applications. The ultimate goal is to bridge the gap between computationally expensive high-fidelity simulations and the need for rapid, reliable predictions of material response in heterogeneous systems.