PINN-based simulation refers to the use of Physics-Informed Neural Networks (PINNs) to solve and simulate problems governed by partial differential equations (PDEs) or other physical laws. This approach combines the power of deep learning with the rigor of physical modeling, offering a new paradigm for computational simulations in a variety of scientific and engineering domains.