DeepONet (Original Paper)

DeepONet uses two sub-networks: a branch net encoding the input function and a trunk net encoding the evaluation location, whose inner product produces the output function value.

Why it matters: It turns the classical universal approximation theorem for operators into a trainable architecture, and remains the standard baseline for operator-learning comparisons.