Strain Generation

Strain generation research focuses on accurately modeling and predicting strain in diverse systems, from soft robots and biological tissues to engineered materials and structures. Current efforts leverage advanced computational methods, including deep learning architectures like deep operator networks and physics-informed neural networks, along with model order reduction techniques such as Proper Orthogonal Decomposition, to improve the efficiency and accuracy of strain prediction. These advancements are crucial for optimizing designs in robotics, biomedicine, and civil engineering, as well as for improving injury risk assessment and enhancing the performance of complex systems. The ability to accurately predict and control strain is vital for advancing numerous fields.