Cosmology -- the study of the origin, evolution, and constituents of the Universe -- is in a scientifically very exciting phase. Two decades of surveying the sky have culminated in the celebrated 'Cosmological Standard Model'. Yet, two of its key pillars, dark matter and dark energy -- together accounting for 95% of the mass-energy of the Universe -- remain mysterious. Deep fundamental questions demand answers: What is dark matter made of? Why is the Universe's expansion rate accelerating? Should general relativity be modified? What is the nature of primordial fluctuations? What is the exact geometry of the Universe? To address these burning questions, survey capabilities are being exponentially improved. Next-generation observatories will open new routes to understand the true nature of the 'Dark Universe'. These observations will pose tremendous challenges on many fronts -- from the sheer size of the data that will be collected (more than a hundred Petabytes) to its modeling and interpretation. The interpretation of the data requires sophisticated simulations on the world's largest supercomputers. The cost of these simulations, the uncertainties in our modeling abilities, and the fact that we have only one Universe that we can observe opposed to carrying out controlled experiments, all come together to create a major test for computational, statistical, and data analysis methods.

In this talk I will give a very brief introduction to the Dark Universe and outline the challenges ahead. To combat these challenges, close cross-disciplinary collaborations between physicists, statisticians, and computer scientists will be crucial. As an example, I will discuss the 'Cosmic Calibration Framework' that we have developed recently to build fast prediction tools for cosmological observables from a small set of very costly large scale simulations. I will conclude with a proposal on how the underlying simulations can be made accessible to the wider community thereby amplifying and enriching the scientific output.