Large Scale Simulation

Large-scale simulation aims to model complex systems with high fidelity and efficiency, addressing the challenges of massive datasets and computational cost. Current research focuses on developing novel algorithms and architectures, such as neural networks (including graph neural networks and implicit neural representations), to accelerate simulations and improve data compression techniques, particularly for agent-based models, microscopic traffic simulators, and molecular dynamics. These advancements are crucial for accelerating scientific discovery across diverse fields, from astrophysics and materials science to robotics and transportation optimization, by enabling more realistic and comprehensive simulations of complex phenomena.