Physics-informed neural networks (PINNs), also referred to as Theory-Trained Neural Networks (TTNs), are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).

Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks.

There are many types of artificial neural networks.

Bidirectional recurrent neural networks connect two hidden layers of opposite directions to the same output.

Region-based Convolutional Neural Networks are a family of machine learning models for computer vision, and specifically object detection and localization.