DeepONet learns nonlinear operators for differential equations via branch and trunk sub-networks, achieving high-order error convergence on small datasets.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2representative citing papers
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.
citing papers explorer
-
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
DeepONet learns nonlinear operators for differential equations via branch and trunk sub-networks, achieving high-order error convergence on small datasets.
-
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.