Modelling Force-Free Neutron Star Magnetospheres using Physics-Informed Neural Networks

Understand

Using Physics-Informed Neural Networks (PINNs) to solve a specific boundary value problem is becoming more popular as an alternative to traditional methods.

• However, depending on the specific problem, they could be computationally expensive and potentially less accurate.
• The functionality of PINNs for real-world physical problems can significantly improve if they become more flexible and adaptable.
• To address this, our work explores the idea of training a PINN for general boundary conditions and source terms expressed through a limited number of coefficients, introduced as additional inputs in the network.