Robots are increasingly becoming an important part of our day-to-day lives, automating tasks such as keeping our homes clean, and picking/packing our parcels at large warehouses. An aging population and the need to substitute human workers in dangerous and repetitive tasks have now resulted in new tasks on the horizon (e.g., in agriculture automation and environmental monitoring), requiring our robots to do more, to work in large-numbers as part of a swarm (a large team of robots), to coordinately sense and act over vast areas, and efficiently perform their mission. However, our robot swarms to date are unprepared for deployment; unable to deal with the inevitable damages and faults sustained during operation, they remain frail systems that cease functioning in difficult conditions. The goal of this project is to remedy this situation by developing algorithms for robot swarms to rapidly -- in no more than a few minutes -- recover from faults and damages sustained by robots of the swarm. The existing fault-tolerant systems for robot swarms are limited. They are constrained to only diagnose faults anticipated a priori by the designer, which can hardly encompass all the possible scenarios a robot swarm may encounter while operating in complex environments for extended periods of time. The multitude of robots in a swarm and the large number of intricate ways they can interact with each other makes it difficult to predict potential faults and predefine corresponding recovery strategies; which may explain why none of the existing fault-detection and fault-diagnosis systems have been extended to provide fault-recovery mechanisms for robot swarms. Therefore, in order to design fault-tolerant algorithms for robot swarms, we need to move beyond the traditional approaches relying on fault-diagnosis information for fault recovery. Fault recovery in a robot swarm may instead be formulated as an online behavior-adaptation process. With such an approach, the robots of the swarm adapt their behavior to sustained faults by learning via trial-and-error new compensatory behaviors that work despite the faults. However, the current approaches to learning new robot swarm behaviors are time-consuming, requiring several hours. Therefore, such approaches are inappropriate for behavior adaptation (learning new swarm behaviors) for rapid fault recovery. Behavior adaptation for effective fault recovery requires the robot swarm to creatively and rapidly learn new compensatory swarm behaviors online, that work despite the sustained faults, effectively recovering the swarm from the faults. The proposal will address these requirements by investigating data-efficient machine learning techniques for rapid online behavior adaptation, guided by creatively and automatically generated intuitions -- evolved offline -- of working swarm behaviors. The resulting system would have a significant impact on long-term operations of robot swarms, and open up new and interesting applications for their deployment, such as the monitoring of large bodies of water for pollutants using a swarm of autonomous surface vehicles.

The outcomes of SYnthesizing 3D METAmaterials for RF, Microwave and THz Applications (SYMETA) have the potential for significant academic, economic, societal and environmental impacts. To achieve these outcomes SYMETA will bring together leading expertise in engineering, physics and materials science from five institutions: Loughborough University, University of Exeter, University of Sheffield, Oxford University and Queen Mary, University of London together with twelve industrial partners from a range of sectors including defence and electronics manufacture. The Grand Challenge will be led by Loughborough University. SYMETA responds to Grand Challenge 3: Engineering across length scales, from atoms to applications. This Challenge area requires researchers to consider design across the scales for both products and systems looking at new approaches to bridge the meso-scale (intermediate-scale) gap and taking into consideration that many engineering systems are dynamic. SYMETA's grand vision is to deliver a palette of novel, multi-functional 3D metamaterials (synthetic composite materials with structure that exhibit properties not usually found in natural materials) using emerging additive manufacturing (AM), with the potential to support a single 'design-build' process. Our goal, to compile a palette of meta-atoms (the basic building blocks of metamaterials) and then to organise these inclusions systematically to give the desired bulk properties, opens up a plethora of new structures. This will not only improve existing applications but inspire new applications by breaking down barriers to innovation. Introducing these novel structures into the complex world of electronic design will offer a radical new way of designing and manufacturing electronics. The metamaterials will be developed to give end-users the electromagnetic responses they require, for a wide range of communication, electronics, energy and defence applications. The meta-atoms comprising the metamaterial will be micro-scale, i.e. small in comparison to the wavelength of operation, and fabricated from a range of new and existing raw materials, including the incorporation of dielectric, metallic and magnetic components. They will facilitate complex multi-component systems, incorporating elements such as inductors, capacitors, and resistors through to transmission lines and matching circuits and filters, to be created in hybrid and multi system AM - reducing waste, cost and timescales. The SYMETA project has three overarching research goals: 1. To synthesize a palette of 3D meta-atoms using suitable materials. 2. To construct designer-specified 3D arrangements of meta-atoms using process efficient AM to create metamaterials 3. To build demonstrators for applications at RF, microwave and THz frequency ranges. Supplementing these research goals SYMETA will: 4. Build a cohort of new knowledge by bringing together multi-disciplinary expertise from a number of institutions and companies and share this knowledge across academic networks. 5. Engage industry, sector relevant professional bodies and the wider academic community to ensure that the potential of this research is recognised and realised. To translate and condense the exciting science to key messages and outcomes and to communicate these to the public to boost the public understanding of science. The likely impacts of the SYMETA are manifold. It has the potential to transform manufacturing processes and to significantly shorten the time it takes for innovative new technologies to reach consumers whilst reducing waste and removing some of the more harmful processes associated with the manufacturing such as the use of harsh chemicals. This is transformation science, which could place the UK at the leading edge of engineering innovation stimulating economic growth and opening up huge potential for innovation in many sectors from consumer electronics through to defence and space.

The scientific discipline of fluid dynamics is primarily concerned with the measurement, modelling and underlying physics and mathematics of how liquids and gases behave. Almost all natural and manufactured systems involve the flow of fluids, which are often complex. Consequently, an understanding of fluid dynamics is integral to addressing major societal challenges including industrial competitiveness, environmental resilience, the transition to net-zero and improvements to health and healthcare. Fluid dynamics is essential to the transition of the energy sector to a low-carbon future (for example, fluid dynamics simulations coupled with control algorithms can significantly increase wind farm efficiency). It is vital to our understanding and mitigation of climate change, including extreme weather events (for example in designing flood mitigation schemes). It is key to the digitisation of manufacturing through 3d printing/additive manufacturing and development of new greener processing technologies. In healthcare, computational fluid dynamics in combination with MRI scanning provides individualised modelling of the cardio-vascular system enabling implants such as stents to be designed and tested on computers. Fluid dynamics also shows how to design urban environments and ventilate buildings to prevent the build-up of pollutants and the transmission of pathogens. The UK has long been a world-leader in fluid dynamics research. However, the field is now advancing rapidly in response to the demand to address more complex and interwoven problems on ever-faster timescales. Data-driven fluid dynamics is a major area where there are rapid advances, with the increasing application of data-science and machine learning techniques to fluid flow data, as well as the use of Artificial Intelligence to accelerate computational simulations. For the UK to maintain its competitive position requires an investment in training the next generation of research leaders who have experience of developing and applying these new techniques and approaches to fluids problems, along with professional and problem-solving skills to lead the successful adoption of these approaches in industry and research. The University of Leeds is distinctive through the breadth, depth and unified structure of its fluid dynamics research, coordinated through the Leeds Institute for Fluid Dynamics (LIFD), making it an ideal host for this CDT. The CDT in Future Fluid Dynamics (FFD-CDT) will build on the experience of successfully running a CDT in Fluid Dynamics to address these new and exciting needs. Our students will carry out cutting-edge research developing new fluid dynamics approaches and applying them across a diverse range of engineering, physics, computing, environmental and physiological challenges. We will recruit and train cohorts of students with diverse backgrounds, covering engineering, mathematical, physical and environmental sciences, in both the fundamental principles of fluid dynamics and new data-driven methodologies. Alongside this technical training we will provide a team-based, problem-led programme of professional skills training co-developed with industry to equip our graduates with the leadership, team-working and entrepreneurial skills that they need to succeed in their future careers. We will build an inclusive, diverse and welcoming community that supports cross-disciplinary science and effective and productive collaborations and partnerships. Our CDT cohort will be at the heart of growing this capability, integrated with and within the Leeds Institute for Fluid Dynamics to deliver a dynamic and vibrant training and research environment with strong UK and international partnerships in academia, industry, policy and outreach.

The achievements of modern research and their rapid progress from theory to application are increasingly underpinned by computation. Computational approaches are often hailed as a new third pillar of science - in addition to empirical and theoretical work. While its breadth makes computation almost as ubiquitous as mathematics as a key tool in science and engineering, it is a much younger discipline and stands to benefit enormously from building increased capacity and increased efforts towards integration, standardization, and professionalism. The development of new ideas and techniques in computing is extremely rapid, the progress enabled by these breakthroughs is enormous, and their impact on society is substantial: modern technologies ranging from the Airbus 380, MRI scans and smartphone CPUs could not have been developed without computer simulation; progress on major scientific questions from climate change to astronomy are driven by the results from computational models; major investment decisions are underwritten by computational modelling. Furthermore, simulation modelling is emerging as a key tool within domains experiencing a data revolution such as biomedicine and finance. This progress has been enabled through the rapid increase of computational power, and was based in the past on an increased rate at which computing instructions in the processor can be carried out. However, this clock rate cannot be increased much further and in recent computational architectures (such as GPU, Intel Phi) additional computational power is now provided through having (of the order of) hundreds of computational cores in the same unit. This opens up potential for new order of magnitude performance improvements but requires additional specialist training in parallel programming and computational methods to be able to tap into and exploit this opportunity. Computational advances are enabled by new hardware, and innovations in algorithms, numerical methods and simulation techniques, and application of best practice in scientific computational modelling. The most effective progress and highest impact can be obtained by combining, linking and simultaneously exploiting step changes in hardware, software, methods and skills. However, good computational science training is scarce, especially at post-graduate level. The Centre for Doctoral Training in Next Generation Computational Modelling will develop 55+ graduate students to address this skills gap. Trained as future leaders in Computational Modelling, they will form the core of a community of computational modellers crossing disciplinary boundaries, constantly working to transfer the latest computational advances to related fields. By tackling cutting-edge research from fields such as Computational Engineering, Advanced Materials, Autonomous Systems and Health, whilst communicating their advances and working together with a world-leading group of academic and industrial computational modellers, the students will be perfectly equipped to drive advanced computing over the coming decades.

Metamaterials are artificial materials with characteristics beyond those found in nature that unlock routes to material and device functionalities not available using conventional approaches. Their electromagnetic, acoustic or mechanical behaviour is not simply dictated by averaging out the properties of their constituent elements, but emerge from the precise control of geometry, arrangement, alignment, material composition, shape, size and density of their constituent elements. In terms of applications, metamaterials have phenomenal potential, in important areas, from energy to ICT, defence & security, aerospace, and healthcare. Numerous market research studies predict very significant growth over the next decade, for example, by 2030 the metamaterial device market is expected to reach a value of over $10bn (Lux Research 2019). The 'Metamaterials' topic is inherently interdisciplinary, spanning advanced materials (plasmonics, active materials, RF, high index contrast, 2D materials, phase change materials, transparent conductive oxides, soft materials), theoretical physics, quantum physics, chemistry, biology, engineering (mechanical and electrical), acoustics, computer sciences (e.g. artificial intelligence, high performance computing), and robotics. Historically, the UK has been a global leader in the field, with its roots in the work of radar engineers in the 2nd World War, and being reinvigorated by the research of some of our most eminent academics, including Professor Sir John Pendry. However today, it risks falling behind the curve. As a specific example, the Chinese government has funded the development of the world's first large-scale metamaterial fabrication facility, which has capacity to produce 100,000 m2 of metamaterial plates annually, with projects relating to aerospace, communication, satellite and military applications. The breadth of metamaterial research challenges is huge, from theory, fabrication, experiment, and requiring expertise in large-scale manufacturing and field testing for successful exploitation. We believe that the isolation of research groups and lack of platforms to exchange and develop ideas currently inhibits the UK's access to the interdisciplinary potential existing within our universities, industries, and governmental agencies. It is of the utmost importance to develop interactions and mobility between these communities, to enable knowledge transfer, innovation, and a greater understanding of the barriers and opportunities. The intervention that this Network will provide will ensure that the UK does not lag our international competitors. Via the Network's Special Interest Groups, Forums, National Symposia and other community-strengthening strategies, the enhanced collaboration will help resolve key interdisciplinary challenges and foster the required talent pipeline across academia and industry. As a result we will see an increase in research power for the metamaterials theme, and therefore reaping the impact opportunities of this area for UK economy and society. The Network's extensive promotion of the benefits of metamaterials technology (e.g., case studies, white papers etc), facilitation of access to metamaterial experts and facilities (through the online database) and closer interactions with end-users at appropriate events (e.g. industry-academia workshops) will help grow external investment in metamaterials research. Ultimately the Network will provide the stimulation of a discovery-innovation-enterprise cycle to meet desired outcomes for prosperity and consequentially, society, defence, and security.