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.
Optimal experimental design: Formulations and computations
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Establishes structural equivalence between finite-time controllability Gramian decomposition and approximate OED information matrix, mapping VCS to D-optimality and AECS to A-optimality.
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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.
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Relationship Between Controllability Scoring and Optimal Experimental Design
Establishes structural equivalence between finite-time controllability Gramian decomposition and approximate OED information matrix, mapping VCS to D-optimality and AECS to A-optimality.