Abstract

Scientific models are often expressed as large and complicated programs. These programs embody numerous assumptions made by the developer (e.g. for differential equations, the discretization strategy and resolution). The complexity and pervasiveness of these assumptions means that often the only true description of the model is the software itself. This has led various researchers to call for scientists to publish their source code along with their papers. I argue that this is unlikely to be beneficial since it is almost impossible to separate implementation assumptions from the original scientific intent. Instead I advocate higher-level abstractions in programming languages, coupled with lightweight verification techniques such as specification and type systems. In this talk, I suggest several possible techniques and outline an evolutionary approach to applying these to existing and future models. One-dimensional heat flow is used as an example throughout. This is joint work with Andrew Rice. The talk is a practice talk for ICCS 2014 (International Conference on Computational Science).