Speaker
Description
Monte Carlo simulations are essential for physics analyses and detector design in High Energy Physics (HEP). As the computational requirements of traditional simulation rapidly outpace available computing budgets, leveraging Generative AI for fast simulation has become vital to producing the required volume of simulated samples.
Nevertheless, transitioning from initial, toy models to full-scale production remains a major challenge. This review highlights the current landscape and operational deployment of generative fast simulation across major HEP experiments. We will evaluate leading architectural paradigms and address practical solutions, including framework integration, validation, and key lessons learned when moving from prototype to full-scale production.