ChatGPT for Programming Numerical Problems of Fluid Mechanics

ChatGPT is a large language model produced by OpenAI. We investigate the performance of ChatGPT for generating numerical and machine learning codes for executing fluid dynamics problems. Specifically, we consider the diffusion equation, the incompressible Navier-Stokes equations, the Euler equations for compressible inviscid flow, etc. Moreover, we look ChatGPT's capabilities for coding physics-informed neural networks and convolutional neural networks for fluid flow predictions. We discuss aspects of both the successes and failures of ChatGPT. Generating singular matrices and arrays with incompatible sizes are examples of the malfunction of ChatGPT. All in all, we conclude that although ChatGPT is a promising tool for generating codes in the area of scientific computing, it requires a significant improvement for generating numerical programs for challenging and serious large-scale fluid mechanics problems.