This minisymposium focuses on the integration of Artificial Intelligence (AI) and High-Performance Computing (HPC) to tackle complex challenges in computational mechanics. With the growing need for large-scale, nonlinear, and multi-physics simulations, the combination of AI and HPC provides promising avenues to improve computational efficiency, accuracy, and scalability.

The session will explore cutting-edge topics, such as AI-driven surrogate models that expedite simulations by reducing the need for exhaustive computations, adaptive algorithms leveraging AI for large-scale mechanical systems, and AI’s role in uncertainty quantification and data assimilation. The fusion of AI with HPC will also be a topic of interest, exploring the new possibilities it offers for handling extreme-scale simulations in near real-time, as well as addressing the challenges posed by heterogeneous and dynamically changing computational environments.

By gathering experts from the fields of computational mechanics, AI, and HPC, the symposium aims to foster discussions on state-of-the-art methodologies and the future potential of AI and HPC in solving some of the most demanding problems in computational mechanics. The topics of this minisymposium include but are not limited to:
• AI-based surrogate models
• AI-enhanced scalable solvers
• High-performance computing with AI acceleration
• Hybrid models integrating AI and traditional methods
• Machine learning in multi-scale ad Multiphysics simulations
• Adaptive algorithms using AI
• AI for uncertainty quantification and data assimilation
• Scalability challenges in AI-enhanced HPC systems