Realistic Simulation

Realistic simulation aims to create accurate virtual representations of real-world phenomena, enabling cost-effective testing and analysis across diverse fields. Current research emphasizes developing high-fidelity simulators using generative models, graph networks, and neural radiance fields, often incorporating real-world datasets to improve accuracy and address the "sim-to-real" gap. These advancements are crucial for applications ranging from autonomous driving and robotics to training AI agents and evaluating security systems, offering significant improvements in efficiency and safety. The focus is on scalability, realism, and the development of benchmarks for evaluating simulation performance.