This perspective proposes a roadmap to addressing and understanding various kinds of complexity, filling in the missing mesoscale links in the knowledge system, and thus tackling significant challenges in various fields as well. In this scenario, case studies on individual complex systems help collect the pristine results. Through exploring common features, general principles are revealed that can guide mesoscale modeling. Effective mesoscale analysis, along with the accumulated big data and improved computing capability, will eventually yield the mesoscale knowledge and hopefully help tackle various challenges and remove technical bottlenecks. Additionally, the relationships among complexity, the knowledge system, research and development activities, and various challenges have been revealed from this logic. Some common understandings and the relevant concept of mesoscience have been referred as possible clues, and several complex systems have been analyzed as introductory examples, though still very preliminarily.

这种观点提出了解决和理解各种复杂性的路线图，填补了知识系统中缺失的中尺度联系，从而也解决了各个领域中的重大挑战。在这种情况下，对单个复杂系统的案例研究有助于收集原始结果。通过探索共同的特征，揭示了可以指导中尺度建模的一般原理。有效的中尺度分析以及累积的大数据和改进的计算能力将最终产生中尺度知识，并有望帮助解决各种挑战并消除技术瓶颈。此外，此逻辑还揭示了复杂性，知识系统，研发活动以及各种挑战之间的关系。