Critical evaluation of PINN for FWD inverse analysis and differentiable FEM as an alternative
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. This study critically evaluates PINN-based inverse analysis for a multilayer pavement system and investigates differentiable finite element method (DiffFEM) as an alternative based on a synthetic benchmark. The standard PINN does not recover layer moduli because of the sharp domain discontinuities inherent to layered pavement systems. Alt...
| Authors | Yongjin Choi, Hyeonbin Moon, Seunghwa Ryu |
|---|---|
| Primary category | cs.CE |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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