DeepONet
Deep Operator Network (DeepONet) is a neural operator architecture that uses a branch-trunk dual-network structure to approximate the solution operators of partial differential equations.
Timeline (last 90 days)
Neural operators are a class of deep learning models that learn mappings between infinite-dimensional function spaces, aiming to approximate the solution operator of PDEs, are resolution-independent, and representative architectures include FNO and DeepONet.
FNO, DeepONet, or other neural operator architectures can all directly integrate the ∂-NO enhancement without redesigning the network structure.
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Neural operators are a class of deep learning models that learn mappings between infinite-dimensional function spaces, aiming to approximate the solution operator of PDEs, are resolution-independent, and representative architectures include FNO and DeepONet.
50%UnverifiedFNO, DeepONet, or other neural operator architectures can all directly integrate the ∂-NO enhancement without redesigning the network structure.
50%