DeepONet learns nonlinear operators for differential equations via branch and trunk sub-networks, achieving high-order error convergence on small datasets.
Nonlineardynamicsystemidentificationusingpipelinedfunctionallinkartificial recurrent neural network.Neurocomputing, 72(13-15):3046–3054
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2019 1verdicts
CONDITIONAL 1representative citing papers
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.