Integrating Physics-Informed Neural Networks for Earthquake Modeling: Physics-Informed Deep Learning | HackerNoon

The Physics-Informed Neural Network (PINN) is a deep learning framework for approximating solutions to PDEs, particularly for problems where traditional numerical methods are inadequate. This framework provides an analytic solution that is continuous and defined at every point in the domain, allowing off-grid evaluation without the need to resolve the PDE. PINN can handle both forward and inverse problems within the same computational setup.

The PINN architecture extends feed-forward deep neural networks to enforce physical conditions specified by an initial-boundary value problem (IBVP). This extension allows for solving complex PDEs with specific application details, offering a mesh-free, efficient approach to solving physics-driven problems.

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