Showing 1 - 2 of 2 UKAEA Paper Results
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UKAEA-CCFE-PR(25)3182024
In recent years, Physics-Informed Neural Networks (PINNs) have gained popularity, across different engineering disciplines, as an alternative to conventional numerical techniques for solving partial differential equations (PDEs). PINNs are physics-based deep learning frameworks that seamlessly integrate the measurements and the PDE in a multitask l…
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UKAEA-CCFE-PR(23)1862023
In recent years, physics-informed neural networks (PINN) have been used to solve stiff-PDEs mostly in the 1D and 2D spatial domain. PINNs still experience issues solving 3D problems, especially, problems with conflicting boundary conditions at adjacent edges and corners. These problems have discontinuous solutions at edges and corners that are di…
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