<ul><li>A physics-informed neural network (PINN) framework is developed for detecting hidden solid boundaries and reconstructing flow fields from sparse observations in fluid mechanics.</li><li>The PINN framework enforces no-slip/no-penetration boundary conditions and conservation laws of fluid dynamics while inferring the presence, shape, and motion of solid boundaries.</li><li>The method successfully reconstructs flow fields and identifies solid boundaries using partial flow field data in various scenarios, including incompressible and compressible flows.</li><li>The proposed method demonstrates robustness and versatility, making it suitable for applications with limited data availability.</li></ul>

Physics-informed neural networks for hidden boundary detection and flow field reconstruction

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