数字孪生：生态学中的动态模型-数据融合。

Digital twins: dynamic model-data fusion for ecology.

机构信息

Wageningen University and Research, Environmental Systems Analysis Group, P.O. Box 47, 6700, AA, Wageningen, The Netherlands.

Nederlandse organisatie voor toegepast natuurwetenschappenlijk onderzoek - TNO, Department of Monitoring & Control Services, Eemsgolaan 3, 9727 DW Groningen, The Netherlands.

出版信息

Trends Ecol Evol. 2023 Oct;38(10):916-926. doi: 10.1016/j.tree.2023.04.010. Epub 2023 May 18.

DOI:10.1016/j.tree.2023.04.010

PMID:37208222

Abstract

Digital twins (DTs) are an emerging phenomenon in the public and private sectors as a new tool to monitor and understand systems and processes. DTs have the potential to change the status quo in ecology as part of its digital transformation. However, it is important to avoid misguided developments by managing expectations about DTs. We stress that DTs are not just big models of everything, containing big data and machine learning. Rather, the strength of DTs is in combining data, models, and domain knowledge, and their continuous alignment with the real world. We suggest that researchers and stakeholders exercise caution in DT development, keeping in mind that many of the strengths and challenges of computational modelling in ecology also apply to DTs.

摘要

数字孪生（DT）是公共和私营部门中的一个新兴现象，是一种用于监测和理解系统和流程的新工具。DT 有可能改变生态学的现状，成为其数字化转型的一部分。然而，通过管理对 DT 的期望来避免错误的发展是很重要的。我们强调，DT 不仅仅是包含大数据和机器学习的一切的大型模型。相反，DT 的优势在于结合数据、模型和领域知识，以及它们与现实世界的持续对齐。我们建议研究人员和利益相关者在 DT 开发中谨慎行事，牢记生态学中计算模型的许多优势和挑战也适用于 DT。