A physics-informed deep operator network infers globally consistent acoustic surface admittance spectra from near-field measurements while enforcing the Helmholtz equation, momentum equation, and Robin boundary conditions during training.
PHYSICS-INFORMED NEURAL OPERATORS Neural operators constitute a class of machine learn- ing methods that aim to approximate mappings between infinite-dimensional function spaces
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Physics-informed neural operators for the in situ characterization of locally reacting sound absorbers
A physics-informed deep operator network infers globally consistent acoustic surface admittance spectra from near-field measurements while enforcing the Helmholtz equation, momentum equation, and Robin boundary conditions during training.