Machine learning–accelerated computational fluid dynamics

Significance Accurate simulation of fluids is important for many science and engineering problems but is very computationally demanding. In contrast, machine-learning models can approximate physics very quickly but at the cost of accuracy. Here we show that using machine learning inside traditional fluid simulations can improve both accuracy and speed, even on examples very different from the training data. Our approach opens the door to applying machine learning to large-scale physical modeling tasks like airplane design and climate prediction.