Emulating deviations from Einstein's General Relativity using conditional GANs

Computationally expensive simulations pose a severe bottleneck, especially in astronomy, where several realizations of the same physical processes are required to facilitate scientific studies, such as exploring new physics or constraining the underlying physics by comparing it with observations. Simulations that modify Einstein's gravity require solving highly non-linear equations and take $\sim$10 times more time than the normal ones. In order to mitigate this bottleneck, we use a conditional generative adversarial network (cGAN) to map output fields from normal simulations to output fields of time-consuming simulations. Our model uses a frequency-based loss during training and uses indirect emulation wherein the mapping is achieved using ratio fields instead of the traditional input $\rightarrow$ output domain translation. Our cGAN agrees well with the ground-truth images despite the visually minor differences between fields from the input and output domains.