FOSSA scores sensor importance for PINN inverse problems via first-order optimality conditions at convergence and shows that low-importance sensors can degrade reconstruction accuracy in electrocardiographic imaging.
Examining the robustness of physics-informed neural net- works to noise for inverse problems
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PINN achieves 91% accuracy in 3D noisy heat diffusion vs 36% for FDM and 3.3x better error reduction in physical experiment, with efficiency gains in high dimensions.
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FOSSA: First-Order Optimality-Based Sensor Selection for PINN Inverse Problems, with Application to Electrocardiographic Imaging
FOSSA scores sensor importance for PINN inverse problems via first-order optimality conditions at convergence and shows that low-importance sensors can degrade reconstruction accuracy in electrocardiographic imaging.