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.
A-pinn: Auxiliary physics informed neural networks for forward and inverse problems of nonlinear integro-differential equations
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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.