Physics-informed neural nets. Introduction:

Physics-Informed Neural Networks (PINNs) are a powerful tool for simulating complex physical systems. Unlike traditional machine learning models, PINNs can effectively utilize limited data by incorporating the underlying physics of the studied system. In scientific and engineering applications, acquiring large labeled datasets can be difficult due to the high cost and limited experimental or simulated data availability. Traditional machine learning models, such as decision trees or support vector machines, require large amounts of labeled data for effective training. However, PINNs can leverage the governing laws and constraints of the studied problem to achieve accurate results with minimal training data.