We will train a cohort of 65 PhD students to tackle the challenge of Data Creativity for the 21st century digital economy. In partnership with over 40 industry and academic partners, our students will establish the technologies and methods to enable producers and consumers to co-create smarter products in smarter ways and so establish trust in the use of personal data. Data is widely recognised by industry as being the 'fuel' that powers the economy. However, the highly personal nature of much data has raised concerns about privacy and ownership that threaten to undermine consumers' trust. Unlocking the economic potential of personal data while tackling societal concerns demands a new approach that balances the ability to innovate new products with building trust and ensuring compliance with a complex regulatory framework. This requires PhD students with a deep appreciation of the capabilities of emerging technology, the ability to innovate new products, but also an understanding of how this can be done in a responsible way. Our approach to this challenge is one of Data Creativity - enabling people to take control of their data and exercise greater agency by becoming creative consumers who actively co-create more trusted products. Driven by the needs of industry, public sector and third sector partners who have so far committed £1.6M of direct and £2.8M of in kind funding, we will explore multiple sectors including Fast Moving Consumer Goods and Food; Creative Industries; Health and Wellbeing; Personal Finance; and Smart Mobility and how it can unlock synergies between these. Our partners also represent interests in enabling technologies and the cross cutting concerns of privacy and security. Each student will work with industry, public, third sector or international partners to ensure that their research is grounded in real user needs, maximising its impact while also enhancing their future employability. External partners will be involved in PhD co-design, supervision, training, providing resources, hosting placements, setting industry-led challenge projects and steering. Addressing the challenges of Data Creativity demands a multi-disciplinary approach that combines expertise in technology development and human-centred methods with domain expertise across key sectors of the economy. Our students will be situated within Horizon, a leading centre for Digital Economy research and a vibrant environment that draws together a national research Hub, CDT and a network of over 100 industry, academic and international partners. We currently provide access to a network of >80 potential supervisors, ranging from leading Professors to talented early career researchers. This extends to academic partners at other Universities who will be involved in co-hosting and supervising our students, including the Centre for Computing and Social Responsibility at De Montfort University. We run an integrated four-year training programme that features: a bespoke core covering key topics in Future Products, Enabling Technologies, Innovation and Responsibility; optional advanced specialist modules; internship and international exchanges; industry-led challenge projects; training in research methods and professional skills; modules dedicated to the PhD proposal, planning and write up; and many opportunities for cross-cohort collaboration including our annual industry conference, retreat and summer schools. Our Impact Fund supports students in deepening the impact of their research. Horizon has EDI considerations embedded throughout, from consideration of equal opportunities in recruitment to ensuring that we deliver an inclusive environment which supports diversity of needs and backgrounds in the student experience.

Artificial intelligence (AI) is on the verge of widespread deployment in ways that will impact our everyday lives. It might do so in the form of self-driving cars or of navigation systems optimising routes on the basis of real-time traffic information. It might do so through smart homes, in which usage of high-power devices is timed intelligently based on real- time forecasts of renewable generation. It might do so by automatically coordinating emergency vehicles in the event of a major incident, natural or man-made, or by coordinating swarms of small robots collectively engaged in some task, such as search-and-rescue. Much of the research on AI to date has focused on optimising the performance of a single agent carrying out a single well-specified task. There has been little work so far on emergent properties of systems in which large numbers of such agents are deployed, and the resulting interactions. Such interactions could end up disturbing the environments for which the agents have been optimised. For instance, if a large number of self-driving cars simultaneously choose the same route based on real-time information, it could overload roads on that route. If a large number of smart homes simultaneously switch devices on in response to an increase in wind energy generation, it could destabilise the power grid. If a large number of stock-trading algorithmic agents respond similarly to new information, it could destabilise financial markets. Thus, the emergent effects of interactions between autonomous agents inevitably modify their operating environment, raising significant concerns about the predictability and robustness of critical infrastructure networks. At the same time, they offer the prospect of optimising distributed AI systems to take advantage of cooperation, information sharing, and collective learning. The key future challenge is therefore to design distributed systems of interacting AIs that can exploit synergies in collective behaviour, while being resilient to unwanted emergent effects. Biological evolution has addressed many such challenges, with social insects such as ants and bees being an example of highly complex and well-adapted responses emerging at the colony level from the actions of very simple individual agents! The goal of this project is to develop the mathematical foundations for understanding and exploiting the emergent features of complex systems composed of relatively simple agents. While there has already been considerable research on such problems, the novelty of this project is in the use of information theory to study fundamental mathematical limits on learning and optimisation in such systems. Information theory is a branch of mathematics that is ideally suited to address such questions. Insights from this study will be used to inform the development of new algorithms for artificial agents operating in environments composed of large numbers of interacting agents. The project will bring together mathematicians working in information theory, network science and complex systems with engineers and computer scientists working on machine learning, AI and robotics. The aim goal is to translate theoretical insights into algorithms that are deployed onreal world applications real systems; lessons learned from deploying and testing the algorithms in interacting systems will be used to refine models and algorithms in a virtuous circle.

This project will extend and enhance the Firedrake automated finite element simulation system to allow researchers across the field of continuum mechanics to simulate a wider range of physical phenomena using more sophisticated techniques than they would be able to code themselves, and to do so by specifying the simulation from highly productive mathematical interface embedded in Python. The simulation of continuous physical systems described by partial differential equations (PDEs) is a mainstay activity of computational science. This spans the integrity of structures, the efficiency of industrial processes built on fluid flow, and the propagation of electromagnetic waves from an antenna to name but a few. Each simulation demands the choice of an appropriate PDE, an accurate and stable discretisation, the efficient parallel assembly of the resulting matrices and vectors, and the fast, scalable solution of the resulting numerical system. Every simulation is the composition of a chain of processes, each of which is a research domain in its own right. Most computational continuum mechanics research happens in small teams. These groups constantly tackle new problems, needing changes at every level of the simulation chain. The challenge is to allow individual researchers and small teams to put together their own simulations, without requiring the impossible by every researcher becoming an expert on the implementation of every stage of the process. Firedrake employs a mathematical language embedded in Python that enables researchers to write the simulation they wish to execute in a highly productive and concise way. The high performance parallel implementation of the simulation is then automatically generated by specialised compilers at runtime. The result is a system in which scientists and engineers write maths and get simulation. This frees researchers to focus on the continuum mechanics question at hand rather than the mechanics of creating the simulation. Firedrake is a widely employed community code with hundreds of published applications across continuum mechanics. For many researchers, Firedrake clearly already meets at least some of their needs. However, the sophistication of continuum mechanics research is boundless: there are always users and potential users whose problems cannot fully be expressed in Firedrake's high level mathematical language. This project will address several such limitations, chosen in response to formal Firedrake user engagement over the last two years. First, we will extend Firedrake's capabilities in solving coupled multi-domain systems. This will enable Firedrake users to more effectively tackle simulation challenges such as the impact of sea waves on wind turbine columns. Second, we will extend Firedrake's automated inverse capabilities to include complex-valued problems. This will significantly benefit users wishing to simulate optimal design problems involving electromagnetic waves. Third, we will extend the range of meshes that Firedrake can employ to include unstructured hexahedral meshes, and hierarchically refined meshes. This will improve Firedrake's support for efficient high order discontinuous Galerkin discretisations and for multiscale problems such as folding of materials. In addition to extending Firedrake's technical capabilities, this project will grow and support the community of continuum mechanics researchers using Firedrake. We will reduce the technical knowledge needed to install Firedrake by providing packages for the main desktop operating systems. We will run tutorials, workshops, and provide online support to new and existing Firedrake users. An "open door" programme of user visits to the Firedrake core developers will provide personal one on one assistance with their simulation needs. We will invest significant time in the extension and maintenance of Firedrake's high quality documentation.

Clouds have a profound influence on weather and climate. Formation of cloud droplets by condensation of water vapour on particles has been studied for many decades. For inert involatile particles, this process and its impacts are relatively well understood. However, a substantial proportion of fine particle material can evaporate under some atmospheric conditions. Our recent Nature Geoscience Letter suggests that the role of this fraction on cloud droplet formation is large enough to be globally significant, is not normally considered in cloud parcel models and is completely untreated in large-scale models. This results from the co-condensation of partly volatile material along with the water vapour during droplet activation. Indirect evidence supports this effect, but direct measurements are unavailable. There has also been considerable interest in the potential role of amorphous "glassy" particles as seeds for ice crystals in cold and mixed-phase clouds. The Nature publication and subsequent work by project partner Virtanen identified that secondary organic aerosol from both biogenic and anthropogenic precursors could exist in an amorphous state dependent on relative humidity and temperature. The impact of glassy particles as ice nuclei is potentially very significant, but direct evidence is currently confused and realistic supporting measurements are sparse. It is proposed to quantify the impacts of organic components on warm and cold cloud formation by both processes through simulation chamber measurements, to use the measurements to evaluate a recently developed model treatment, to parameterise the model and use the parameterisation to quantify the regional impacts on cloud physical and radiative properties. We have conducted proof of concept laboratory work showing that we are able to study both processes. We have coupled the Manchester Aerosol Chamber (MAC), where we can make particles from the atmospheric chemistry of both natural plant emissions and man-made emissions, to the Manchester Ice Cloud Chamber (MICC), where we can form a cloud under reasonable atmospheric conditions. We have further measured the changes in the effectiveness of the particles to act as seeds for liquid cloud droplets, cloud condensation nuclei (CCN), along with the volatility, composition and phase behaviour. We propose to build on this proof-of-concept to systematically quantify the effects in a range of atmospherically-representative systems and quantify their impacts. The proposed work will be carried out in 4 parts. The first two are laboratory-based with numerical model interpretation and the second two solely use numerical modelling: i) quantification of the effect of organic vapours in two instruments that are used in the field and laboratory, one measuring particle water uptake below 100% RH and the other the ability to form a cloud droplet just above 100% RH. Particles will be exposed to controlled concentration of semi-volatile vapour and introduced into the instruments. Detailed flow modelling of the second instrument will be carried out, in collaboration with the author as project partner. ii) involves the coupling of the MAC and MICC chambers as in the proof-of-concept, but covering particles formed in a wide range of natural, manmade and mixed systems. We will measure all relevant parameters to quantify the formation of warm and cold clouds under a reasonable range of atmospheric conditions. iii) informed by the experiments, the effects of organic compounds on warm and cold clouds will be included in a numerical model and this will be used to develop physically-based parameterisations for use in large-scale models. iv) the parameterised process description will be used in large-scale models informed by our project partner Nenes to estimate the impact on cloud properties and radiation, hence quantifying the couplings between organic compounds and weather and climate under representative conditions.

The Innovative Manufacturing and Construction Research Centre (IMCRC) will undertake a wide variety of work in the Manufacturing, Construction and product design areas. The work will be contained within 5 programmes:1. Transforming Organisations / Providing individuals, organisations, sectors and regions with the dynamic and innovative capability to thrive in a complex and uncertain future2. High Value Assets / Delivering tools, techniques and designs to maximise the through-life value of high capital cost, long life physical assets3. Healthy & Secure Future / Meeting the growing need for products & environments that promote health, safety and security4. Next Generation Technologies / The future materials, processes, production and information systems to deliver products to the customer5. Customised Products / The design and optimisation techniques to deliver customer specific products.Academics within the Loughborough IMCRC have an internationally leading track record in these areas and a history of strong collaborations to gear IMCRC capabilities with the complementary strengths of external groups.Innovative activities are increasingly distributed across the value chain. The impressive scope of the IMCRC helps us mirror this industrial reality, and enhances knowledge transfer. This advantage of the size and diversity of activities within the IMCRC compared with other smaller UK centres gives the Loughborough IMCRC a leading role in this technology and value chain integration area. Loughborough IMCRC as by far the biggest IMRC (in terms of number of academics, researchers and in funding) can take a more holistic approach and has the skills to generate, identify and integrate expertise from elsewhere as required. Therefore, a large proportion of the Centre funding (approximately 50%) will be allocated to Integration projects or Grand Challenges that cover a spectrum of expertise.The Centre covers a wide range of activities from Concept to Creation.The activities of the Centre will take place in collaboration with the world's best researchers in the UK and abroad. The academics within the Centre will be organised into 3 Research Units so that they can be co-ordinated effectively and can cooperate on Programmes.