Fast Simulation

Fast simulation aims to drastically reduce the computational cost of simulating complex systems, from atomic transport in materials to large-scale particle collisions and even video games, enabling faster research and development. Current research heavily utilizes machine learning, employing generative models like diffusion models, GANs, and VAEs, as well as neural PDE solvers and graph neural networks, to create accurate yet computationally efficient surrogate models. These advancements significantly accelerate simulations across diverse fields, impacting areas such as materials science, high-energy physics, robotics, and design verification by enabling larger-scale and more detailed analyses previously infeasible due to computational limitations.