Capturing functional relations in fluid-structure interaction via machine learning

Tejas Soni, Ashwani Sharma, Rajdeep Dutta, Annwesha Dutta, Senthilnath Jayavelu & Saikat Sarkar
While fluid-structure interaction (FSI) problems are ubiquitous in various applications from cell-biology to aerodynamics, they involve huge computational overhead. In this paper, we adopt a machine learning (ML)-based strategy to bypass the detailed FSI analysis that requires cumbersome simulations in solving the Navier-Stokes (N-S) equations. To mimic the effect of fluid on an immersed beam, we have introduced dissipation into the beam model with time-varying forces acting on it. The forces in a discretized setup...
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