A Compositional Framework for Scientific Model Augmentation
This addresses the challenge for scientists in efficiently managing and expanding complex computational models, though it appears incremental as it builds on existing program analysis techniques.
The paper tackles the problem of augmenting, combining, and comparing scientific computational models by proposing SemanticModels.jl, a system that uses program analysis and a category theory-based framework to automate metamodeling tasks, resulting in enhanced scientific workflows as demonstrated in a case study.
Scientists construct and analyze computational models to understand the world. That understanding comes from efforts to augment, combine, and compare models of related phenomena. We propose SemanticModels.jl, a system that leverages techniques from static and dynamic program analysis to process executable versions of scientific models to perform such metamodeling tasks. By framing these metamodeling tasks as metaprogramming problems, SemanticModels.jl enables writing programs that generate and expand models. To this end, we present a category theory-based framework for defining metamodeling tasks, and extracting semantic information from model implementations, and show how this framework can be used to enhance scientific workflows in a working case study.