Zongyi works on developing deep learning methods (neural operators) for partial differential equations and scientific applications. Problems in science and engineering involve solving partial differential equations (PDE) systems. Sometimes, these PDEs are very hard. Data-driven, learning-based methods have the promise to solve these problems faster and more accurately. The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces or finite sets. To better approximate the solution operators raised in PDEs, we propose a generalization of neural networks to learn operators mapping between infinite-dimensional function spaces. Such neural operators are resolution-invariant, and consequently more efficient compared to traditional neural networks. Especially, the Fourier neural operator model has shown state-of-the-art performance with 1000x speedup in learning turbulent Navier-Stokes equation, as well as promising applications in weather forecast and carbon capture simulation.