The article discusses how scientists use computer models to study complex phenomena. These models are run multiple times with different inputs to gather data. Since running these models can be expensive, researchers aim to create cheaper predictors for the output. They achieve this by treating the deterministic output as a random process, allowing for better experiment design and prediction accuracy. The approach also provides estimates of prediction uncertainty. The article reviews recent work in this area, presents various applications, and demonstrates the methodology with an example.