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Error Analysis of Physics-Informed Neural Networks for Approximating Dynamic PDEs of Second Order in Time
Our analyses show that, with feed-forward neural networks having two hidden layers and the $\tanh$ activation function, the PINN approximation errors for the solution field, its time derivative and its gradient field can be effectively bounded by the training loss and the number of training data points (quadrature points).
A multi-category inverse design neural network and its application to diblock copolymers
The neural network model consists of two parts, a classifier and Structure-Parameter-Mapping (SPM) subnets.
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