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
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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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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Overcoming the Limits of Finite Difference Method; Physics-Informed Neural Network for Noisy High-Dimensional Heat Diffusion
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.