Digital technologies and services are shaping our lives. Work, education, finance, health, politics and society are all affected. They also raise concomitant and complex challenges relating to the security of and trust in systems and data. TIPS (Trust, Identity, Privacy and Security) issues thus lie at the heart of our adoption of new technologies and are critical to our economic prosperity and the well-being of our citizens. Identifying and addressing such issues requires a coherent, coordinated, multi-disciplinary approach, with strong stakeholder relationships at the centre. SPRITE+ is a vehicle for communication, engagement, and collaboration for people involved in research, practice, and policy relevant to TIPS in digital contexts. Since launching in 2019, we have established ourselves as the go-to point of contact to engage with the broadest UK network of interdisciplinary, cross-sector digital TIPS experts. The second phase of SPRITE+ ('SPRITE+2') will continue to build our membership, whilst expanding the breadth and depth of our innovation, and deepen our impact through proactive engagement. SPRITE+2 will have the following objectives: 1. Expand our TIPS community, harnessing the expertise and collaborative potential of the national and international TIPS communities 2. Identify and prioritise future TIPS research challenges 3. Explore and develop priority research areas to enhance our collective understanding of future global TIPS challenges 4. Stimulate innovative research through sandpits, industry led calls, and horizon scanning 5. Deepen engagement with TIPS research end users across sectors to accelerate knowledge Exchange 6. Understand, inform, and influence policy making and practice at regional, national and international level These will be delivered through four work packages and two cross cutting activities. All work packages will be led by the PI (Elliot) to ensure that connections are made and synergies exploited. Each sub-work package will be led by a member of the Management Team and supported by our Expert Fellows and Project Partners. WP1 Develop the Network We will deliver a set of activities designed to expand, broaden, and engage the network, from expert meetings and workshops to student bootcamps and international conferences. WP2 Engage stakeholders to enhance knowledge exchange and deliver impact. We will be greatly enhancing our purposive engagement activity in SPRITE+2. This activity will include a new business intelligence function and PP engagement grants, designed to enhance mutual understanding between researchers and stakeholders. WP3 Identify, prioritise, and explore future TIPS challenges We will select and then investigate priority areas of future TIPS. Two areas are pre-scoped based on the work we have done so far in SPRITE+ (TIPS in digital cities; trustworthy digital identities) with a further two be identified during the lead up to SPRITE+2. WP4 Drive innovation in research This WP concerns the initiation and production of high-quality impactful research. Through horizon scanning, sandpits and industry-led calls, we will steer ideas through an innovation pipeline ensuring SPRITE+2 is future focused. Cross cutting activities The first cross-cutting activity will accelerate the translation of TIPS research into policy and practice for public and private sector end uses. The second focuses on mechanisms to facilitate communication within our community. The experiences of SPRITE+ and the other DE Network+s demonstrate that it takes years of consistent and considerable effort for a new network to grow membership and develop productive relationships with stakeholders. In SPRITE+2 grant we would hit the ground running and maximise the impact of four additional years of funding. A successful track record, a well-established team, and a raft of ambitious new plans provide a solid foundation for strong delivery in 2023-27.

DATA-CENTRIC will fundamentally transform modern computational engineering through the development of algorithms that are accountable. This means algorithms capable of quantifying the uncertainty arising from computation itself, delivering simulations that are more transparent, traceable and at the same time more efficient. Crucial decisions in science, engineering, healthcare and public policy rely on established methodologies such as the Finite Element Method and the Stochastic Finite Element Method. However, the models that inform such decisions suffer from an inevitable loss of accuracy due to, and not limited to the following sources of uncertainty: a) time and cost constraints of running modern high-fidelity computer models, b) simplifying approximations necessary to translate mathematical models into computational models, and c) limited numerical precision inherent to any computer system. Therefore, there is a continuous risk of relying on unverified computational evidence, and the path from modelling to decision-making can be (inadvertently or unwillingly) obscured by the lack of accountability. DATA-CENTIC will solve this problem through Probabilistic Numerics, a framework that will enable decision-makers to monitor, diagnose and control the quality of computer simulations. Probabilistic Numerics treats computation as a statistical problem, thus enriching computation with a probabilistic measure of numerical error. This idea is gathering momentum, especially in the UK. However, theoretical development are still in their early stages and except for a few examples, it has not been applied to solve large-scale industrial problems. Consequently, it has not yet been adopted by industry. DATA-CENTRIC will bridge this gap. . The proposed approach will provide radically new insights into the Finite Element Method and the Stochastic Finite Element Method. In particular, it will produce new solutions to industrial problems in Biomechanics and Robust Design. This has the potential of transforming personalised medicine and high-value manufacturing and will open the door to new industrial applications.

The Materials Made Smarter Centre has been co-created by Academia and Industry as a response to the pressing need to revolutionise the way we manufacture and value materials in our economy. The UK's ability to manufacture advanced materials underpins our ambitions to move towards cleaner growth and a more resource efficient economy. Innovation towards a net zero-carbon economy needs new materials with enhanced properties, performance and functionality and new processing technologies, with enhanced manufacturing capability, to make and deliver economic and societal benefit to the UK. However, significant technological challenges must still be overcome before we can benefit fully from the transformative technical and environmental benefits that new materials and manufacturing processes may bring. Our capacity to monitor and control material properties both during manufacture and through into service affect our ability to deliver a tailored and guaranteed performance that is 'right-first-time' and limit capacity to manage materials as assets through their lifetime. This reduces materials to the status of a commodity - a status which is both undeserved and unsustainable. Future materials intensive manufacturing needs to add greater value to the materials we use, be that through reduction of environmental impact, extension of product life or via enhanced functionality. Digitalisation of the materials thread will help to enhance their value by developing the tools and means to certify, monitor and control materials in-process and in-service improving productivity and stimulating new business models. Our vision is to put the UK's materials intensive manufacturing industries at the forefront of the UK's technological advancement and green recovery from the dual impacts of COVID and rapid environmental change. We will develop the advanced digital technologies and tools to enable the verification, validation, certification and traceability of materials manufacturing and work with partners to address the challenges of digital adoption. Digitisation of the materials thread will drive productivity improvements in materials intensive industries, realise new business models and change the way we value and use materials.

There have recently been significant leaps in deep reinforcement learning algorithms, with notable successes in games such as Atari arcade games and Go; however, there is still a need to adapt these techniques to be more widely applicable in other domains, such as the life science sector. Identifying regulatory relationships between genes is one of the primary research activities carried out by molecular biologists and geneticists, since learning the structure of gene regulatory networks is critical for many applications, for example understanding the origins of many diseases and how crops respond to their environments. Biologists sequentially conduct experiments that provide information about the gene network structure, but they must operate under strict cost and time limits. This project aims to formulate this experiment design procedure in a reinforcement-learning framework, to ascertain how biologists should prioritise experiments to maximise information about the gene networks, under constraints. The primary deliverable will be a Computer-aided Experimental Design (CoED) software tool to aid researchers in utilising their resources most effectively. This reinforcement-learning framework could also be used to identify the bottlenecks for biomedical research, such as the pricing model or the time-intensity of certain experiments, thereby identifying the most impactful areas for further development in experimental methodology. We will deliver impact by providing consultation services to laboratory supply and service providers, and through our collaboration with our industrial partner Google Brain Genomics. This project primarily aligns with the new approaches to data science and high productivity services through specialised artificial intelligence priority areas of this call.

PREMIERE will integrate challenges identified by the EPSRC Prosperity Outcomes and the Industrial Strategy Challenge Fund (ISCF) in healthcare (Healthy Nation), energy (Resilient Nation), manufacturing and digital technologies (Resilient Nation, Productive Nation) as areas to drive economic growth. The programme will bring together a multi-disciplinary team of researchers to create unprecedented impact in these sectors through the creation of a next-generation predictive framework for complex multiphase systems. Importantly, the framework methodology will span purely physics-driven, CFD-mediated solutions at one extreme, and data-centric solutions at the other where the complexity of the phenomena masks the underlying physics. The framework will advance the current state-of-the-art in uncertainty quantification, adjoint sensitivity, data-assimilation, ensemble methods, CFD, and design of experiments to 'blend' the two extremes in order to create ultra-fast multi-fidelity, predictive models, supported by cutting-edge experimental investigations. This transformative technology will be sufficiently generic so as to address a wide spectrum of challenges across the ISCF areas, and will empower the user with optimal compromises between off-line (modelling) and on-line (simulation) efforts so as to meet an a priori 'error bar' on the model outputs. The investigators' synergy, and their long-standing industrial collaborations, will ensure that PREMIERE will result in a paradigm-shift in multiphase flow research worldwide. We will demonstrate our capabilities using exemplar challenges, of central importance to their respective sectors in close collaboration with our industrial and healthcare partners. Our PREMIERE framework will provide novel and more efficient manufacturing processes, reliable design tools for the oil-and-gas industry, which remove conservatism in design, improve safety management, and reduce emissions and carbon footprint. This framework will also provide enabling technology for the design, operation, and optimisation of the next-generation nuclear reactors, and associated reprocessing, as well as patient-specific therapies for diseases such as acute compartment syndrome.