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.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equa- tions,
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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.