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Deep operator network (DeepONet) is a machine learning framework designed to learn nonlinear operators mapping between infinite-dimensional function spaces. It extends deep learning approaches to functional inputs and outputs, enabling efficient approximation of complex operators arising in scientific computing and engineering.
The Fourier neural operator is a machine learning framework designed to learn mappings between function spaces, particularly useful for solving partial differential equations (PDEs). It uses Fourier transforms to efficiently represent and learn operators, enabling generalization across different discretizations.
Graph neural operators are computational frameworks that extend graph neural networks to learn operators mapping between function spaces defined on graphs. They are used to model complex systems and solve partial differential equations on irregular domains by learning mappings that generalize across different graph structures.