Physics-informed Neural Networks (PINNs) are deep learning-based methods for predicting solutions of partial differential equations (PDEs). Unlike conventional machine learning approaches, PINNs do not rely solely on data; instead, they directly incorporate physical laws, expressed as PDEs, into the loss functions used for training neural networks. This feature allows PINNs to offer a unified framework suitable for solving a wide range of PDE problems, including both forward and inverse problems, making them broadly applicable in science and engineering. In this talk, I will briefly introduce the basic concepts of PINNs, followed by several illustrative applications of PINNs to simple PDE problems.

강연시간 : 16:40-17:10