Most PL research is concerned with the engineering of software. Programs, in the narrative driving this work, are human-built artifacts. The PL research challenge is to make sure that they can be engineered, with as little effort as possible, to be reliable and have high performance.
But programming languages can also be used for modeling natural phenomena. This is the central goal in the discipline of computational science, which develops tools for programmatically modeling real-world processes ranging from nuclear reactions to climate change to protein synthesis.
PL research has always played a critical role in computational science. It is impossible to simulate complex natural processes without high-performance computing (HPC), and advances in HPC are often, really, advances in compilers and runtime systems.
However, a new, intriguing path for PL ideas to shape our understanding of the natural world has opened up in the recent past. This path concerns not just the representation of the natural world as programs, but also the automatic, data-driven discovery of these programs through program synthesis.