A Doctoral Training Centre in Complex Systems Simulations

当前复杂性科学研究的激增是由新理论、紧迫的现实挑战和利用前所未有的计算能力的机会驱动的。南安普顿DTC复杂系统仿真（CSS）将是第一个在跨学科博士研究项目的背景下结合这三个组成部分的明确，互补和综合培训。这将推动使能模拟技术和复杂性科学方法的进步，以及气候、制药、生物科学、纳米科学、医疗和化学系统、运输、工程和计算等关键应用领域的现场研究问题。与20世纪以原子、遗传和分子科学为代表的还原论研究项目取得的巨大成功相比，未来的世纪将越来越多地关注系统科学和工程，以物理、生物、环境、社会和技术系统及其相互作用为目标，并解决系统功能、组织、管理、稳定性、弹性和可进化性等问题。除了跨学科和高通量实验，模拟建模已经成为解决这些问题的关键工具。随着廉价计算能力的现成可用性，模拟构建正在成为博士生在广泛学科领域的可行选择。然而，认识到如何最好地部署计算资源以理解复杂目标系统的培训目前非常不完整，这反映了目前在相对孤立的研究领域进行的复杂性科学研究的碎片化性质，这一点至关重要。虽然下一代研究人员肯定会使用强大的模拟，但尚不清楚他们是否会有效和严格地使用它们。这种情况必须立即通过在临界数量的研究人员中巩固最佳实践来解决。有效的培训必须在适当的复杂系统领域的实时研究挑战的背景下进行。它必须解决技术实现的问题，以及如何利用高性能计算的最新技术，但也要灌输广泛的方法复杂性。特别是，掌握仿真、数学建模和实验之间的相互依存关系是至关重要的，正如掌握仿真建模方法的不同优缺点一样，从有限元方法到多尺度模型和系统的系统方法到基于代理的建模，从通过抽象计算思维实验解决数学模型的有效数值方法到现实的预测仿真模型。为了实现这一目标，南安普顿DTC CSS将培养一批博士毕业生，他们将成为研究领域的领导者，将复杂系统模拟应用于21世纪最紧迫的科学和工程挑战。

The current surge in complexity science research is being driven by new theory, pressing real-world challenges and the opportunity to exploit an unprecedented availability of computational power. The Southampton DTC in Complex Systems Simulation (CSS) will be the first to combine explicit, complementary and integrated training in these three components in the context of interdisciplinary doctoral research projects. This will drive progress in both the enabling simulation technologies and complexity science methodology, as well as live research questions in key application domains spanning climate, pharma, biosciences, nanoscience, medical and chemical systems, transport, engineering & computing.In contrast to the twentieth century's huge success in reductionist research programmes epitomised by atomic, genetic and molecular science, the coming century will see an increasing focus on systemic science and engineering, targeting physical, biological, environmental, social, and technological systems and their interactions, and addressing questions of system function, organisation, management, stability, resilience, and evolvability. Alongside interdisciplinarity and high-throughput experimentation, simulation modelling is already emerging as the key tool for addressing such questions. With the ready availability of cheap computational power, simulation building is becoming a viable option for doctoral students across a wide range of disciplines. However, it is critical to recognise that training in how best to deploy computational resources to understand complex target systems is currently extremely patchy, reflecting the currently fragmented nature of complexity science research being carried out across relatively isolated research domains. While the next generation of researchers will certainly be using powerful simulations it is not yet clear that they will be using them effectively and rigorously. This situation must be addressed immediately by consolidating best practice in a critical mass of researchers.Effective training must be delivered in the context of live research challenges within appropriate complex systems domains. It must address issues of technical implementation and how to exploit the state of the art in high-performance computing, but also inculcate broad methodological sophistication. In particular, command of the interdependent relationship between simulation, math modelling, and experimentation is crucial, as is a grasp of the different strengths and weaknesses of simulation modelling approaches ranging from finite element methods through multi-scale models and systems-of-systems approaches to agent-based modelling, and from efficient numerical methods for solving math models through abstract computational thought experiments to realistic predictive simulation models. In achieving this, the Southampton DTC CSS will generate a community of doctoral graduates equipped to act as research leaders in applying complex systems simulation to the most pressing scientific and engineering challenges of the 21st century.