Learn/Core Concept Why use physics-informed neural networks? Physics-informed neural networks (PINNs) embed known physical laws directly into neural network training by adding physics equations as loss function constraints. Unlike pure data-driven models, PINNs respect conservation laws, boundary conditions, and differential equations governing the system. This approach is particularly powerful for engineering simulations where you have limited training data but strong physical understanding. The NVIDIA PhysicsNeMo tutorial shows how PINNs can model fluid dynamics with fewer samples than traditional ML, making them ideal for scientific computing where physics matters more than pure pattern matching. Surrogate ModelsNeural ODEsScientific ML |