For a long time, computational scientists have relied on Moore's law (bigger, faster, cheaper) to realize their complex computational workflows on ever more powerful machines. Recently, however, this bastion of the digital world has started to show some cracks - the required speedups are no longer guaranteed simply by waiting a year or two, or investing in larger systems - clearly an alternative approach is needed.
We present the concept of Intelligent Simulations as the answer to this call. In an intelligent simulation, we ask the system to work smarter, not harder. This can include replacing expensive parts of the model with a cheaper surrogate, or iterating through batches of simulations in such as way that minimal redundant information is determined. A key player in this regime is a technique known as Bayesian optimization, and throughout this talk, I will demonstrate how we have built upon ancient mathematical foundations to build a new tool for the digital age.