A learned coordinate-mapping neural network is inserted in front of a PINN so that PDEs on irregular domains can be solved in a unit reference domain with autograd Jacobians and hard boundary constraints, improving accuracy in the tested 2D/3D cases.
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Solved in Unit Domain: JacobiNet for Differentiable Coordinate-Transformed PINNs
A learned coordinate-mapping neural network is inserted in front of a PINN so that PDEs on irregular domains can be solved in a unit reference domain with autograd Jacobians and hard boundary constraints, improving accuracy in the tested 2D/3D cases.