Direct Numerical Simulation

Direct numerical simulation (DNS) is a computationally intensive method for accurately modeling complex fluid flows and other physical phenomena governed by partial differential equations. Current research focuses on accelerating DNS through machine learning techniques, employing architectures like neural operators (including Fourier and physics-enhanced variants), graph networks, and transformers to improve efficiency and scalability. These advancements aim to overcome the limitations of traditional DNS, enabling more realistic and detailed simulations across diverse fields such as climate science, materials science, and high-energy physics, ultimately leading to improved predictions and design optimization.