A physics-informed neural framework reconstructs unsteady fluid-structure interactions from sparse off-body Lagrangian particle tracks by combining modal surface models with coordinate neural representations constrained by fluid governing equations and interface conditions.
Fourier features let networks learn high frequency functions in low dimensional domains,
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Neural inference of fluid-structure interactions from sparse off-body measurements
A physics-informed neural framework reconstructs unsteady fluid-structure interactions from sparse off-body Lagrangian particle tracks by combining modal surface models with coordinate neural representations constrained by fluid governing equations and interface conditions.