To create many of the complex products and systems we have around us we have needed advanced technology. But to create the volume and complexity of products we have also needed complex organisational systems and processes. Large complex organisations have in particular relied on the Systems Engineering process, to help guide complex projects to completion. Many products, such as aircraft, only exist because of this systematic approach. But this systematic approach has a downside. To maintain control of a complex design it is necessary to fix ideas and concepts, and work through detail in a top-down approach. This flow down keeps development within the bounds of the original idea or concept, but naturally prevents innovation and variation. Such variation and innovation are in some ways the enemy of the controlled organisation needed to keep a global enterprise on track. One great fear is the phenomenon of emergence; inherently unknowable behaviour. Ironically this kind of innovation is desperately needed to take advantage of the opportunities offered by new technologies, such as additive manufacturing, or distributed cloud based manufacturing. But marrying these technologies within a complex fixed organisational structure and process is very difficult. Building on the success of the Design the Future project "In Search of Design Genes" this work looks to nature for inspiration, for an unconstrained approach to engineering design. Introducing the concept of 'Biohaviour' we follow the behaviour of natural growth rather than biomimicry. The creation of an elemental set of rules based on energy and equilibrium, could allow variation to naturally arise in design. In nature, the rules are applied blindly with no fixed final form. That final form only arising as a consequence of its environment. Trees and bamboo are wonderful examples of this. Our hypothesis is that by reimagining design as a series of elemental rules and growth mechanisms that react to environment and stimuli, the design of complex systems will be simplified, and emergence could be used as a tool for innovation beyond conventional paradigms. We see four major challenges: * Obtaining growth rules for component seeds to allow components to emerge from the activity * Defining stimuli that will make the component seeds grow and establishing if that growth can be controlled via the stimuli. * Developing fast, scalable, event triggered systems to enable real time creation of complex designs. * Capturing the emergent behaviour into a working set of parameters which can interact with existing design and manufacturing systems - i.e. is there a set of parameters which will define a CAD model? In this project we will investigate theoretical aspects of this approach, and the practical implications of using these elementary rules in engineering design. We will develop novel computational methods for fast, scalable, event triggered systems to represent component seeds' growth behaviour, which will create a design depending on the environment around it. The seeds will grow to form a more complete component or system which can be envisioned in a CAD system. The seeds and shoots will have the ability to spawn others as the system develops in response to the environment. For example, forming a branch, or root, or in an engineering context a stiffener or hole. The result should be a set of rules encapsulated in a prototype Cloud service, that will automatically create a component from a simple seed definition. Depending on its surroundings, it will grow large or small, taking form, shape & colour according to need. One seed should be capable of producing a variety of solutions, generating innovation naturally. By tweaking the rules and behaviours we expect to allow some emergent behaviour to occur. This feeds back to the aim of this study - to establish if these elementary rules can be put to effective use in design - and to create the Blind Watchmaker.

SONNETS - Scalability Oriented Novel Networks of Event Triggered Systems - takes a clean-slate approach to next-generation computer modelling and artificial intelligence. To drive this we have an over-arching research goal that is both nationally important and challenging: real-time modelling of UK financial risk. It is easy to identify underlying risks after they cause a financial crisis. With hindsight, the 2008 financial crash was caused by too many banks buying too many risky mortgages. Whilst the crisis was unfolding it was all new information: no-one realised how many banks owned the risky mortgages. Then it was assumed that mortgage defaults were unlikely. Finally, it was assumed that losses in a few banks would not affect the national economy. The problem was a lack of visibility and understanding of the national picture: each bank appeared to have a manageable risk level, but most banks in the UK were exposed to the same underlying risk factor, so once mortgages started defaulting most banks started losing money and a perfect financial storm developed. What we needed then, and still do now, is national-level risk modelling that can consider risk across banks as it occurs. Modelling risk for one bank is a difficult problem, and modelling the entire UK is much harder. Banks have complex constantly changing portfolios, so building a picture of "who owns what" means tracking millions of trades per day. Even if we have that picture we still need to somehow assess risk, but that requires anticipating the future: we must pre-emptively identify potential scenarios, then estimate how much is lost in each scenario. Currently regulators use "stress tests" to identify national risk - they define a possible challenging economic scenario, then ask all the banks to estimate how much they might lose. However, this is both slow - the process takes months - and limited - they only explore one very severe scenario, which probably isn't the one that causes the problem. SONNETS will create a system that performs national-level risk analysis in real-time, by building a "digital twin" of the UK's financial system and using it to continually generate plausible future scenarios and assess their risk. We then use artificial intelligence to learn what risky scenarios look like. This gives regulators completely new tools: - A day-by-day view of the current national-risk of the UK, rather than waiting months for stress tests; - The ability to look forwards to identify and mitigate previously unknown risks as they develop, rather than waiting for a financial crisis to reveal them. We tackle this problem by addressing challenges in three main areas: - Computing: new paradigms for creating and running programs, exploiting multiple types of computer hardware distributed across the cloud; - Artificial Intelligence: methods for continual learning that can be split into multiple pieces, so that learning processes can be moved closer to the data they are learning from; - Modelling: theory and tools for automatic scenario generation, plus the ability to assess risk over large-scale models of the UK's financial institutions. These three areas are tightly linked, with the new computing paradigms supporting execution of the new AI and modelling in the cloud, and a synergistic relationship between the modelling of the system and learning about the model. Underpinning these three areas is the idea of event-triggered computing, where programs are split up into small fragments which send messages to each other. Using this event-triggered approach we can scale the risk analysis system up to support national-level risk analysis. It will constantly assess how risky the UK currently is, while trying to anticipate what scenarios might lead to financial crises in the future. SONNETS will provide a powerful tool to detect and mitigate financial risk as it is building up, rather than trying to react to a financial crisis once it happens.

Future Finance 4 All, led by the University of Bristol, will take a mission led approach to accelerate innovation adoption in Mid-Tier organisations and small and medium-sized enterprises (SME), in the UK Financial Services (FS) Sector across the four UK home nations. The focus of this partnership is to enhance the sector's productivity and global competitiveness. To achieve this, we will develop an understanding, from a social science perspective, of the drivers and obstacles to innovation uptake in this target group. We will then put in place a mission-oriented approach that leverages both leading social science research and experience in supporting SME innovation adoption to inform the development of an innovation adoption accelerator. The accelerator will be delivered over three phases, Phase 1-Local, Phase 2-Regional, Phase 3-National. Working with partners, including policy makers, industry and community organisations, the accelerator will help us overcome obstacles and drive innovation adoption across UK regional FS clusters. This will overcome the market failures that are holding back innovation uptake, unlocking productivity and levelling benefits across the UK regions. The accelerator will also enable us to also tackle societal challenges around responsible access and uptake of FS for underserved communities, individuals and companies. This will lead to the development of new bespoke products and services, which the Mid-Tier organisations and SMEs, which are the accelerator's focus, could then exploit. This potential 'market making opportunity' for new FS product and service innovation could have relevance in both UK and global markets that share similar inclusion challenges. The innovation accelerator activities will facilitate networking and partnerships between social science experts and the financial services community through innovator pathway fellows', drawn from high potential early career researchers. Building on our partnership's research base and expertise supporting innovation clusters, we will then deliver a rolling collaborative challenge programme that brings together industry, academic and social insights to explore and address barriers to innovation adoption. Through a rolling programme and digital platform the challenge programme outputs will inform the development of specific interventions for FS firms and stakeholders to enable them to gain the skills and capabilities to innovate. To maximise engagement and efficiency the innovation skill & training Programme will be delivered in scalable hybrid format and include peer-to-peer learning. Foundational to the Programme will be a focus on inclusive growth and diversifying the talent pipeline, addressing key findings from the 2022 UoB-led FinTech report, Kalifa Review, cross-sector surveys (EY and Innovate Finance, 2022), and sector-wide consultations. The accelerator will support the creation of habit-forming behaviour change through the exploitation of the Quadruple Helix model that brings universities, underserved communities, industry (including the sector's charities and not for profit players) and government to: Better connect key actors across the FS sector to overcome fragmentations, this will build new skills and capabilities within the partners and the project team. Ensure that the voices of underserved communities, individuals and companies are heard and reflected in the tangible delivery of new, or enhanced, FS products and services. Stimulate and support industry to prioritise innovation investment. Provide pathways, and practical solutions, to enable innovation uptake, including digital innovation, that enhances the productivity of mid-tier FS organisations and SMEs. A key project output will the measurement of these productive gains and their impact on the organisations that we support, and how this will contribute towards UK regional levelling up by unlocking a broad spectrum of organisational, economic and social benefits

The DART project aims to pioneer a ground-breaking capability to enhance the performance and energy efficiency of reconfigurable hardware accelerators for next-generation computing systems. This capability will be achieved by a novel foundation for a transformation engine based on heterogeneous graphs for design optimisation and diagnosis. While hardware designers are familiar with transformations by Boolean algebra, the proposed research promotes a design-by-transformation style by providing, for the first time, tools which facilitate experimentation with design transformations and their regulation by meta-programming. These tools will cover design space exploration based on machine learning, and end-to-end tool chains mapping designs captured in multiple source languages to heterogeneous reconfigurable devices targeting cloud computing, Internet-of-Things and supercomputing. The proposed approach will be evaluated through a variety of benchmarks involving hardware acceleration, and through codifying strategies for automating the search of neural architectures for hardware implementation with both high accuracy and high efficiency.

Our society is increasingly reliant upon engineered systems of unprecedented and growing complexity. As our manufacturing and service industries, and the products that they deliver, continue to complexify and interact, and we continue to extend and integrate our physical and digital infrastructure, we are becoming increasingly vulnerable to the cascading and escalating effects of failure in highly complex and evolving systems of systems. Consequently, it is becoming increasingly critical that we are able to understand and manage the risk and uncertainty in Complex Engineering Systems (CES) to provide reliant and optimal design and control solutions. Research on natural complex systems is helping us to understand the implications of inter-dependencies within and between complex adaptive systems. However, unlike natural ecosystems, which may become more robust through diversifying, man-made complex systems tend to become more fragile as their complexity increases. If we are to deal with the challenge presented by complex engineered systems, we will need to exploit and synthesise our current understanding of natural and engineered systems, our current theories of complexity more generally. The ENgineering COmplexity REsilience Network Plus (hereafter called ENCORE) addresses the Grand Challenge area of Risk and Resilience in CES. Our vision is to identify, develop and disseminate new methods to improve the resilience and sustainable long-term performance of complex engineered systems, initially including Cities and National Infrastructure, ICT and Energy Infrastructure, Complex Products: Aerospace (both Jet Engines and Space Launch and Recovery Systems) and later to explore the inclusion of Nuclear Submarines, Power Stations and Battlefield Systems. We have chosen these particular CES domains as they strike a balance between the challenges and opportunities that the UK faces for which complexity science can have a significant impact for our citizens and businesses whilst spanning sufficiently diverse fields to present cross-domain learning opportunities. Our approach is to create shared learning from [1] the manner in which naturally complex systems cope with risk and uncertainty to deliver resilience (ecosystems, climate, finance, physiology, etc.) and how such strategies can be adapted for engineering systems; [2] how the tools and concepts of complexity science can contribute towards developing a greater understanding of risk, uncertainty and resilience, and [3] distilling world-class activity within individual CES domains to provide new insights for the design and management of other engineering systems. Examples of the potential for the application of this field and which will be considered for inclusion in the feasibility studies include: - Predicting equipment failures and their consequences in critical infrastructure systems; - Developing a management heuristic that plays the same role as a "risk register", but addresses systemic resilience; - Optimising the deployment of instrumentation required to manage cities and other CES effectively; - Increasing the resilience of interdependent digital systems; - Advancing models of cascading failure on networks such that they take account of node heterogeneity and in particular the different failure/recovery modes of different types of node. - Improving the number of contexts in which CES can be deployed with replicable performance; - Decreasing the likelihood of human behavioural errors in operating CES. - Identifying the critical elements that constrain/define system performance most strongly; - Extending system lifetimes and functionality; - Mapping the relationship between complex system complexity and fragility; - Characterising uncertainty and defining the inference process to transition from one phase to the other in the control of CES and in complex decision making processes.