Machine learning (ML) and artificial intelligence (AI) have an important role in interpreting the complex data that form an everyday part of modern astrophysics research. However, throughout all of astrophysics, we face a unique challenge in that we cannot directly access a ‘ground truth’ dataset that one would normally employ to train AI/ML methods. In the absence of such training sets, we must create them. Our research group builds, runs, and analyzes massive cosmological simulations as a way to probe the nature structure formation in our Universe. These same simulations can also be used as training sets for AI/ML analysis. In this talk, I’ll start by describing our traditional, computationally intensive approaches to modeling the Universe. I’ll then discuss two specific examples where simulation trained ML methods have come to play an important role by improving our ability to invert highly-complex, non-linear functions and back-out physical properties that would otherwise be nearly impossible to access. Finally, I’ll conclude by discussing some of the future prospects within the astrophysics and cosmology research spaces for applications of AI/ML analysis.