Learning high-order geometric flow based on the level set method

Recently, the development of deep learning has accomplished unbelievable success in many fields, especially scientific computational fields. And almost all problems and physical phenomena can be described by partial differential equations. In this work, we proposed two potential high-order geometric flows. Motivation physical-information neural networks traditional level set method (LSM), have integrated LSM to make more robust efficient. Also, test sensitivity system different input data, up three sets initial conditions model. Furthermore, numerical experiments on data are implemented demonstrate effectiveness superiority models compared state-of-the-art approach.