Newton Neural Preconditioner with Interface Awareness for CZM

Cohesive zone models (CZM) are fundamental in the numerical analysis of fracture in composite materials, especially in aerospace applications. However, simulating delamination or fiber debonding presents significant computational challenges due to nonlinearity and discontinuities in the response. Techniques such as viscous regularization or manual modification of the Newton-Raphson method often alter the physics of the problem or increase computational cost. In this context, an innovative approach based on artificial intelligence offers a promising alternative: an interface-aware neural preconditioner (IA-NNP) that acts exclusively on the active interface variables, preserving the original separation-traction law and improving convergence in difficult increments. This method uses interface graphs to encode the local state, and a neural network learns to correct the initial estimate or precondition the iterations, ensuring that the final solution matches that of the discrete model. For companies developing advanced simulation software, integrating artificial intelligence into numerical algorithms represents a qualitative leap. At Q2BSTUDIO, we understand the need for AI solutions for businesses that can optimize complex processes such as structural analysis. Additionally, we offer custom applications that integrate everything from AWS and Azure cloud services to cybersecurity modules, as well as business intelligence tools like Power BI. Our AI agents can be trained for specific tasks, improving efficiency and accuracy in simulation environments. The combination of traditional numerical methods with machine learning opens new frontiers in computer-aided engineering, and at Q2BSTUDIO we accompany our clients in this digital transformation with robust and scalable solutions.