Critical evaluation of PINN for FWD inverse analysis and differentiable FEM as an alternative
arXiv:2606.03210v1 Announce Type: cross Abstract: 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 synthet
The increasing sophistication of AI models and automatic differentiation techniques is enabling more complex engineering problems to be tackled with computational methods.
This research highlights the potential for AI-driven inverse analysis and differentiable programming to significantly enhance the efficiency and accuracy of infrastructure assessment and design.
Traditional, often heuristic, methods for inverse analysis in structural engineering could be replaced or augmented by more robust, gradient-based AI approaches, accelerating discovery and optimization.
- · Civil Engineering Sector
- · AI/ML Engineering Firms
- · Infrastructure Development Companies
- · Computational Scientists
- · Traditional Analytical Software Providers
- · Heuristic Model Developers
- · Consultants reliant on older simulation methods
More accurate and rapid assessment of material properties and structural integrity will become possible.
This could lead to optimized maintenance schedules and extended lifespan for critical infrastructure like pavements and bridges.
The broader adoption of differentiable programming in engineering could foster a new generation of 'AI-native' design and analysis tools across various physical domains.
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Read at arXiv cs.LG