GPU Based

GPU-based acceleration is revolutionizing scientific computing by dramatically speeding up simulations and training of complex models across diverse fields. Current research focuses on leveraging GPUs to enhance reinforcement learning algorithms (like PPO and Q-learning), enabling efficient training of sophisticated models such as graph neural networks and latent dynamics networks for applications in areas like fluid dynamics, cardiology, and robotics. This increased computational power facilitates the development of more accurate and efficient simulations, leading to breakthroughs in areas such as data-driven modeling, physics-informed digital twins, and real-world robotic control. The resulting speed improvements allow for larger-scale simulations and more extensive exploration of design spaces, ultimately accelerating scientific discovery and technological advancement.