Multiple Neural Operators achieve near-optimal approximation and generalization rates for multi-task operator learning, matching single-task scaling laws and performing similarly to a multi-task DeepONet extension.
Exponential convergence of deep operator networks for elliptic partial differential equations.SIAM Journal on Numerical Analysis, 61(3):1513–1545, 2023
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Multiple Neural Operators Achieve Near-Optimal Rates for Multi-Task Learning
Multiple Neural Operators achieve near-optimal approximation and generalization rates for multi-task operator learning, matching single-task scaling laws and performing similarly to a multi-task DeepONet extension.