Advanced Deep Learning Architectures and Techniques

In the previous two chapters, we developed a variety of techniques and concepts that led to the first successful deep learning models. This was only the starting point for a whole avalanche of new network architectures and sophisticated training techniques. In this chapter, we explore a number of state-of-the-art deep learning methods of relevance to materials science and physics. These range from convolutional neural networks and autoencoders to generative adversarial networks and physically informed neural networks. A number of concrete examples help to understand the power and the limitations of such networks. Finally, a brief summary of ongoing developments is given.