A disproportionate amount of the primary motor cortex, a region of the brain that controls movement, is devoted to body parts that move in more complicated ways. An especially large portion controls the face and lips; a similarly large portion controls the hands. A human hand is capable of moving in twenty-seven separate ways, more by far than any other body part: our wrists rotate, our knuckles move independently of one another, our fingers can spread or contract. The sensors in the skin of the hand are among the densest in the body, and are part of a network of nerves that run along the spinal cord. "People think of the spinal column as just wires," Arthur Petron, a roboticist who earned his Ph.D. in biomechatronics at M.I.T., said.

The Eulerian fluid simulation is an important HPC application. The neural network has been applied to accelerate it. The current methods that accelerate the fluid simulation with neural networks lack flexibility and generalization. In this paper, we tackle the above limitation and aim to enhance the applicability of neural networks in the Eulerian fluid simulation. We introduce Smartfluidnet, a framework that automates model generation and application. Given an existing neural network as input, Smartfluidnet generates multiple neural networks before the simulation to meet the execution time and simulation quality requirement. During the simulation, Smartfluidnet dynamically switches the neural networks to make the best efforts to reach the user requirement on simulation quality. Evaluating with 20,480 input problems, we show that Smartfluidnet achieves 1.46x and 590x speedup comparing with a state-of-the-art neural network model and the original fluid simulation respectively on an NVIDIA Titan X Pascal GPU, while providing better simulation quality than the state-of-the-art model.