A PINN trained on sparse FEM data plus Poisson's equation predicts electric field and potential for a capacitive sensor at finger distances from 0 to 25 mm with low overall error.
A combined data-driven and physics-driven method for steady heat conduction prediction using deep convolutional neural networks
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Capacitive Touch Sensor Modeling With a Physics-informed Neural Network and Maxwell's Equations
A PINN trained on sparse FEM data plus Poisson's equation predicts electric field and potential for a capacitive sensor at finger distances from 0 to 25 mm with low overall error.