Stress Strain

Stress-strain relationships describe how materials deform under applied forces, a fundamental concept in materials science and engineering. Current research heavily emphasizes developing accurate and efficient constitutive models, often employing machine learning techniques like deep neural networks, recurrent neural networks, and input convex neural networks, to capture complex material behaviors including viscoelasticity, viscoplasticity, and anisotropy. These data-driven approaches aim to overcome limitations of traditional models, particularly in handling high-dimensional data, uncertainty, and path-dependent responses. Improved constitutive modeling has significant implications for diverse applications, ranging from designing more robust engineering structures to accurately simulating biological tissues.