Particle Dynamic

Particle dynamics research focuses on modeling and simulating the movement and interactions of numerous particles, aiming to efficiently and accurately predict their collective behavior in diverse systems. Current efforts leverage machine learning, employing architectures like graph neural networks, transformers, and physics-informed neural networks to accelerate simulations and solve inverse problems, often applied to fluid dynamics, high-energy physics, and material science. These advancements offer significant potential for improving the speed and accuracy of simulations across various scientific disciplines and enabling new applications in areas such as materials design, fusion energy research, and robotics.