# A Compositional Framework for Scientific Model Augmentation

## TL;DR

SemanticModels.jl is a system that uses program analysis and category theory to automate and enhance the augmentation and comparison of scientific models, improving scientific workflows.

## Contribution

It introduces a novel category theory-based framework and a software system for metamodeling tasks using static and dynamic program analysis techniques.

## Key findings

- Successfully implemented a working case study demonstrating the framework.
- Enhanced scientific workflows through automated model augmentation.
- Provided a formal foundation for metamodeling tasks in scientific modeling.

## Abstract

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.