Learning Objectives:

• Understand the leap from function approximation to operator learning
• Master the Universal Approximation Theorem for Operators
• Learn the DeepONet architecture: Branch and Trunk networks
• Implement operator learning for the derivative operator
• Apply DeepONet to the 1D nonlinear Darcy problem

This topic includes materials that are collected together in Jupyter notebooks that can be run to reproduce the results contained in the topic pages. Access to the notebooks is described in the Lab page at the end of this topic: Lab: DeepONet and Pi-DeepONet. If you would like to run the code in the notebooks as you work through the materials in this topic, consult that Lab page for information on how to proceed.