Self-Adaptive Physics-Informed Neural Networks
(SA-PINNs) are a variation of traditional Physics-Informed Neural Networks (PINNs) designed to
solve the challenges of solving ”stiff” partial differential equations (PDEs). By using adaptive weighting, SA-PINNs are able to focus their attention on areas of the domain with higher errors, therefore improving accuracy. This work investigates the
roles of individual loss components, namely residuals, boundary conditions, and initial conditions, in the performance of SA-PINNs.