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.
Physics-informed neural networks and extensions
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A two-level HPRNN framework is proposed that embeds physical properties into latent spaces to surrogate nonlinear elasto-plastic yarn behavior and meso-to-macro transitions for woven composites.
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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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Multiscale Analysis of Woven Composites Using Hierarchical Physically Recurrent Neural Networks
A two-level HPRNN framework is proposed that embeds physical properties into latent spaces to surrogate nonlinear elasto-plastic yarn behavior and meso-to-macro transitions for woven composites.
- Computational Control of Nonlinear Partial Differential Equations Using Machine Learning