AI RESEARCH
Critical evaluation of PINN for FWD inverse analysis and differentiable FEM as an alternative
arXiv CS.LG
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ArXi:2606.03210v1 Announce Type: cross Automatic-differentiation-based inverse analysis methods, including physics-informed neural networks (PINNs) and differentiable programming, have recently shown great promise due to their ability to compute accurate gradients and convergence efficiency. However, their applicability to falling weight deflectometer (FWD) backcalculation remains unexplored.