J. Pereira

Comparative Study of Physics-Informed Neural Networks (PINNs) and Optimizing a Discrete Loss (ODIL) for Forward and Inverse Problems in Unsteady Non-Linear Partial Differential Equations

This work systematically compares two state-of-the-art frameworks-Physics-Informed Neural Networks (PINNs) and Optimizing a Discrete Loss (ODIL) across benchmark elliptic, hyperbolic, and parabolic problems, culminating in a challenging inverse source reconstruction task.