  Ever since its debut, generative adversarial networks (GANs) have attracted
tremendous amount of attention. Over the past years, different variations of
GANs models have been developed and tailored to different applications in
practice. Meanwhile, some issues regarding the performance and training of GANs
have been noticed and investigated from various theoretical perspectives. This
subchapter will start from an introduction of GANs from an analytical
perspective, then move on to the training of GANs via SDE approximations and
finally discuss some applications of GANs in computing high dimensional MFGs as
well as tackling mathematical finance problems.
