# Shingle 2.0: generalising self-consistent and automated domain discretisation for multi-scale geophysical models

## TL;DR

This paper introduces a novel, automated, and extensible approach for the spatial discretisation of geophysical models, improving reproducibility, provenance tracking, and consistency in multi-scale, unstructured mesh generation.

## Contribution

It presents a generalised, self-documenting framework for domain discretisation that captures heterogeneous parameters and constraints, enabling robust, repeatable, and verifiable mesh generation.

## Key findings

- Automated mesh generation is robust and consistent with source data.
- The approach improves reproducibility and provenance tracking.
- It enables quick drafting and validation of discretisations.

## Abstract

The approaches taken to describe and develop spatial discretisations of the domains required for geophysical simulation models are commonly ad hoc, model or application specific and under-documented. This is particularly acute for simulation models that are flexible in their use of multi-scale, anisotropic, fully unstructured meshes where a relatively large number of heterogeneous parameters are required to constrain their full description. As a consequence, it can be difficult to reproduce simulations, ensure a provenance in model data handling and initialisation, and a challenge to conduct model intercomparisons rigorously. This paper takes a novel approach to spatial discretisation, considering it much like a numerical simulation model problem of its own. It introduces a generalised, extensible, self-documenting approach to carefully describe, and necessarily fully, the constraints over the heterogeneous parameter space that determine how a domain is spatially discretised. This additionally provides a method to accurately record these constraints, using high-level natural language based abstractions, that enables full accounts of provenance, sharing and distribution. Together with this description, a generalised consistent approach to unstructured mesh generation for geophysical models is developed, that is automated, robust and repeatable, quick-to-draft, rigorously verified and consistent to the source data throughout. This interprets the description above to execute a self-consistent spatial discretisation process, which is automatically validated to expected discrete characteristics and metrics.