Challenges of Achieving Efficient Simulations Through Model Abstraction

TL;DR

This paper explores the challenges and solutions in achieving efficient simulations of complex, multi-scale natural systems through model abstraction, using vegetation-landscape models as a case study.

Contribution

It demonstrates how homomorphic modeling can derive coarse-grain models from fine-grain models to improve simulation efficiency in natural system modeling.

Findings

Coarse-grain models reduce simulation time.

Homomorphic modeling enables effective abstraction.

Identifies barriers in modeling complex natural systems.

Abstract

Coupled natural systems are generally modeled at multiple abstraction levels. Both structural scale and behavioral complexity of these models are determinants in the kinds of questions that can be posed and answered. As scale and complexity of models increase, simulation efficiency must increase to resolve tradeoffs between model resolution and simulation time. From this vantage point, we will show some problems and solutions by using as example a vegetation-landscape model where individual plants belonging to different species are represented as collectives that undergo growth and decline cycles spanning hundreds of years. Collective plant entities are assigned to cells of a static, two-dimensional grid. This coarse-grain model, guided by homomorphic modeling ideas, is derived from a fine-grain model representing plants as individual objects. These models are developed using Python and GRASS tools. A set of experiments is devised to reveal some barriers in modeling and simulating this class of systems.