Research problems in the domains of physical, engineering, biological sciences often span multiple time and length scales, owing to the complexity of information transfer underlying mechanisms. Multiscale modeling (MSM) and high‐performance computing (HPC) have emerged as indispensable tools for tackling such complex problems. We review the foundations, historical developments, and current paradigms in MSM. A paradigm shift in MSM implementations is being fueled by the rapid advances and emerging paradigms in HPC at the dawn of exascale computing. Moreover, amidst the explosion of data science, engineering, and medicine, machine learning (ML) integrated with MSM is poised to enhance the capabilities of standard MSM approaches significantly, particularly in the face of increasing problem complexity. The potential to blend MSM, HPC, and ML presents opportunities for unbound innovation and promises to represent the future of MSM and explainable ML that will likely define the fields in the 21st century.

中文翻译：

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多尺度建模调查：基础、历史里程碑、现状和未来前景


由于信息传输基础机制的复杂性，物理、工程、生物科学领域的研究问题往往跨越多个时间和长度尺度。多尺度建模（MSM）和高性能计算（HPC）已成为解决此类复杂问题不可或缺的工具。我们回顾了 MSM 的基础、历史发展和当前范式。在百亿亿次计算初期，HPC 的快速发展和新兴范式正在推动 MSM 实现的范式转变。此外，在数据科学、工程和医学的爆炸式增长中，与 MSM 集成的机器学习 (ML) 有望显着增强标准 MSM 方法的功能，特别是在面对日益复杂的问题时。 MSM、HPC 和 ML 的融合潜力为无限创新提供了机会，并有望代表 MSM 和可解释的 ML 的未来，这可能会定义 21 世纪的领域。