Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning Pu Ren Simon Fraser University Simon Fraser University Lawrence Berkeley National Laboratory Shashank Subramanian

Recent years have witnessed the promise of coupling machine learning methods and physical domain-specific insights for solving scientific problems based on partial differential equations (PDEs). However, being data-intensive, these methods still require a large amount of PDE data.