PINNs Masterclass
Course Material Access Update
We are sharing a small but important update regarding how course materials and source codes for the PINNs Masterclass are accessed.
New PINNs Tutorial Published:
Inverse Modeling of 2D Incompressible Navier–Stokes Equations (Full PyTorch Implementation)
👉 Existing students can access this tutorial anytime from their course dashboard. We just added a fresh, implementation-first tutorial to my PINNs course — and this one is special. In this tutorial, we solve an inverse problem for the 2D incompressible Navier–Stokes equations using a real CFD dataset (cylinder wake flow). We intentionally keep only sparse velocity observations and ask a PINN to reconstruct the full flow physics:
PINNs vs Classical Solvers:
Why We Use PINNs When FEM or FDM Already Work
There is a question I keep hearing, again and again, in different forms. “Are PINNs better than FEM?” “Can PINNs replace finite difference or finite element methods?” “We already have FEM or FDM solving this problem—so why do we need PINNs?” “Why is my PINN so inaccurate compared to a spectral solver?”
From Lagaris to Raissi: The Evolution of PINN Formulations
When people talk about Physics-Informed Neural Networks today, they usually mean one thing: a neural network trained with a PDE residual loss and a few penalty terms for boundary and initial conditions.
What Are Inverse PINNs and Why They Are Harder Than They Look
Suppose you are working with a physical system that you mostly understand. You know the governing equation. You trust it. Heat conduction, diffusion, elasticity, fluid flow — pick one. The math itself is not the issue.