Warwick Complexity Science Doctoral Training Centre 2

更好地理解、适应、设计和控制复杂系统是我们社会面临的一个关键挑战。一个复杂的系统包括许多相互作用的组件，导致多层次的集体结构和组织。例子包括自然系统，从生物分子和活细胞到人类社会系统和生态圈，以及复杂的人工系统，如互联网，电网或任何大规模分布式软件系统。正在进行的研究主题：复杂性，涌现和升级。在面向数学的研究中，我们试图明确信息内容和涌现行为的清晰和适用的定义。复杂流体和复杂流动。一小部分相互作用的粒子是如何合谋控制它们的流动特性的，这些特性又是如何影响特定的流动的？集群、凝聚和干扰。聚类现象无处不在，从雨滴到星系，从facebook到交通堵塞。复杂网络及其动态网络的连通性及其动态之间的相互作用是当今流行病学、生物多样性、神经科学和市场等关键挑战的核心。网络统计推断。网络结构的推断是我们在从分子生物学到健康和经济学等多个领域的应用中使用的关键方法。统计力学的新应用。这套完善的工具在分子生物学、交通理论和意见动态学中有新的用途。发展中领域包括：社会科学、流行病学和相关生态学、生物成像等复杂系统。国家需求。金融危机表明，英国迫切需要受过培训的人才，以了解复杂社会技术系统中的系统性风险，并设计监管和激励措施，使经济重新启动。关于气候变化的辩论表明，对英国来说，让人们接受培训，了解政策对一个庞大复杂的社会经济物理生物系统的影响是多么重要。我们的合作伙伴BAS（英国南极调查局）明确承认复杂性是其组织的一个部门：自然复杂性计划。关于国家卫生服务管理的辩论表明，让受过训练的人参与设计激励和监测系统是多么重要。监测基因表达能力的进步为受过识别模式培训的人提供了巨大的机会，有助于控制许多疾病，特别是癌症。监测大脑活动能力的进步为接受过理解网络功能培训的人创造了巨大的机会，有助于控制癫痫、帕金森病和阿尔茨海默病等功能障碍。

It is a key challenge for our society to better understand, adapt, design and control complex systems. A complex system comprises many interacting components leading to multiple levels of collective structure and organization. Examples include natural systems ranging from bio-molecules and living cells to human social systems and the ecosphere, as well as sophisticated artificial systems such as the Internet, power grid or any large-scale distributed software system. Ongoing Research Themes:Complexity, Emergence & Upscaling. In mathematically oriented research we attempt to crystallise clear and appliable definitions of information content and emergent behaviour.Complex Fluids and Complex flows. How do a small fraction of interacting particles conspire to dominate their flow properties, and how do those properties influence particular flows? Clustering, Condensation and Jamming. Clustering phenomena are ubiquitous with applications ranging from raindrops to galaxies, and from facebook to traffic jams. Complex Networks & their dynamics. The interplay between the connectivity of a network and its dynamics are central to key challenges today, such as epidemiology, biodiversity, neuroscience and markets. Network Statistical Inference. The inference of network structure is a key approach we use in applications spanning multiple fields, from molecular biology to health and economics. New Applications of Statistical Mechanics. This well developed set of tools finds fresh use in molecular biology, traffic theory and opinion dynamics. Developing Areas include:Complex Systems in Social Science, Epidemiology and related Ecology, and Bioimaging.National Need.The financial crisis shows how urgently the UK needs people trained to understand systemic risk in a complex socio-technical system and to design regulation and incentives to get the economy going again. The debate on climate change shows how vital it is to the UK to have people trained in understanding the implications of policies on a large complex socio-economic-physical-biological system. Our partner BAS (British Antarctic Survey) explicitly recognises Complexity as a divisional aspect of their organisation: Natural Complexity Programme. The debate about management of the National Health Service shows how important it is to involve trained people in designing the incentive and monitoring system. The advances in ability to monitor gene expression provide a huge opportunity for people trained in discerning patterns to contribute to controlling many diseases, particularly cancer. The advances in ability to monitor brain activity create huge opportunities for people trained in understanding network function to contribute to controlling malfunctions such as epilepsy, Parkinson's disease and Alzheimer's disease.