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.
On the application of immersed boundary, fictitious domain and body- conformal mesh methods to many particle multiphase flows,
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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.