复杂性数据科学:来自数字双胞胎的衍生产物
Frank Emmert-Streib1, Hocine Cherifi2, Kimmo Kaski3,4
1Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Korkeakoulunkatu 7, 33720 Tampere, Finland.
PNAS nexus
|November 13, 2024
概括
数字双胞胎,虚拟模型结合模拟和学习,被概括为复杂性数据科学. 这个新领域综合了复杂性和数据科学,提供了广泛的含义和机会.
科学领域:
- 多学科科学 多学科科学
- 数据科学是数据科学.
- 复杂性科学是一门复杂性科学.
背景情况:
- 数字双胞胎正在医学 (瘤学,免疫学,心脏学) 中获得吸引力.
- 核心概念包括通过模拟和学习创建物理实体的虚拟模型.
- 现有的应用突出显示了这一潜力,但缺乏一个通用的理论框架.
研究的目的:
- 将数字双胞胎概念推广到更广泛的科学领域.
- 建立理论基础,将数字双胞胎与复杂性和数据科学联系起来.
- 探索这一普遍化的领域的含义,历史,挑战和机会.
主要方法:
- 数字双胞胎概念的理论概括.
- 确定复杂性科学和数据科学的合成.
- 分析跨学科的联系和基础原则.
主要成果:
- 数字双胞胎的普遍化导致了"复杂性数据科学"的出现.
- 这种综合将复杂性科学和数据科学的原则统一起来.
- 这篇论文为这个新兴领域提供了基础框架.
结论:
- 复杂性数据科学提供了一种统一的方法来理解和开发先进的数字生应用程序.
- 这一通用领域对未来的研究和在各个领域的实际实施具有重大潜力.
- 认识到数字双胞胎的二元性为跨学科的科学研究开辟了新的途径.
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