Physical Simulation

Physical simulation aims to accurately model and predict the behavior of physical systems, often using computational methods to solve complex equations governing their dynamics. Current research emphasizes developing more efficient and accurate simulation techniques, focusing on graph neural networks (GNNs), message-passing transformers, and physics-informed machine learning models to improve speed and accuracy, particularly for complex systems like fluids and deformable bodies. These advancements are crucial for various applications, including robotics, autonomous driving, and scientific discovery, by enabling faster and more realistic simulations for design, testing, and analysis.