Constitutive Model

Constitutive modeling aims to mathematically describe the relationship between a material's stress and strain, enabling accurate prediction of its mechanical behavior under various conditions. Current research heavily utilizes machine learning, particularly neural networks (including physics-informed, recurrent, and graph convolutional architectures), to learn these relationships from experimental data or high-fidelity simulations, often bypassing the need for explicitly defined constitutive equations. This approach offers improved accuracy and efficiency for complex materials and scenarios, impacting fields like material science, structural engineering, and biomechanics through more precise simulations and faster material characterization.