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Optimization based simulation model development: Solving robustness issues

机译:基于优化的仿真模型开发:解决鲁棒性问题

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摘要

Mathematical models are becoming popular to represent biological systems. A mathematical model can be based upon existing knowledge from scientific literature, expert opinion, and field and laboratory studies. However, there are significant issues in model development including robustness. This study therefore examines how model quality can be improved automatically using optimization approaches. Specifically, we examine how a recently developed robust model of a forest pest species, with potential application in areas such as risk prediction [3], may have its robustness further increased using optimization. Digital eco-systems provide a powerful and broader methodological foundation and support for the implementation of optimization through application of the design science method1.
机译:数学模型越来越受到生物系统的流行。 数学模型可以基于科学文献,专家意见和现场和实验室研究的现有知识。 但是,在模型开发中存在显着的问题,包括鲁棒性。 因此,本研究检查了如何使用优化方法自动改善型号质量。 具体而言,我们研究了最近开发的森林害虫物种的鲁棒模型,其中在风险预测的区域中具有潜在应用,可以使用优化进一步增加其鲁棒性。 数字生态系统提供了强大且更广泛的方法基础,并通过应用设计科学方法 1 实现优化的支持。

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