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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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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:数学模型正变得越来越流行来代表生物系统。数学模型可以基于来自科学文献,专家意见以及现场和实验室研究的现有知识。但是,模型开发中存在很多问题,包括健壮性。因此,本研究探讨了如何使用优化方法自动提高模型质量。具体来说,我们研究了最近开发的森林病虫害稳健模型如何在风险预测等领域得到潜在应用[3],使用优化可以使其稳健性进一步提高。数字生态系统通过应用设计科学方法 1 为优化的实施提供了强大而广泛的方法论基础。

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