Particle Based

Particle-based methods represent a powerful approach to modeling complex systems by discretizing continuous phenomena into interacting particles. Current research focuses on developing and applying these methods in diverse areas, including robotics (using Gaussian splatting and predictive control), dynamic scene representation (via neural radiance fields and particle filters), and physical simulation (leveraging graph neural networks and transformers). These advancements are significantly impacting fields like computer vision, robotics, and molecular dynamics by enabling more efficient and accurate modeling of dynamic systems and improving the realism of simulations.