A cross-section-based scaling of the loss function accelerates convergence and improves accuracy for MF-PINNs on neutron diffusion problems across 1D-3D and fixed-source to eigenvalue cases.
Physics-constrained neural network for solving discon- tinuous interface K-eigenvalue problem with application to reactor physics
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On Physics-Based Loss Scaling for MF-PINNs applied to the neutron diffusion equation
A cross-section-based scaling of the loss function accelerates convergence and improves accuracy for MF-PINNs on neutron diffusion problems across 1D-3D and fixed-source to eigenvalue cases.