Overall objective of this proposed project is to contribute to smart growth in the European Union by increasing the efficiency and effectiveness of investments in research, development and innovation (both private or public), and by contributing to a better and more efficient connection of actors along the value chain. Specifically, this proposal will contribute to increase the efficiency and effectiveness of innovation projects in SMEs in the region of Bavaria. This will be achieved by the delivery of well-defined services packages to the SMEs, targeting specifically the enhancement of their innovation management capacity and the removal of potential innovation gaps and bottlenecks. Target groups of the service packages will be 1. SME beneficiaries in Bavaria participating in Phase 1 and/or Phase 2 of the Horizon 2020 SME Instrument (“Key Account Management”) 2. Other internationally oriented SMEs in Bavaria with strong ambitions in innovation and growth (“Enhancing SME Innovation Management Capacity”) ‘Innovation management’, as addressed by this project comprises a company’s complete innovation process from idea generation to innovation result. It includes internal enabling factors like leadership, management skills, financial situation, market knowledge etc, as well as external cooperation with third parties like suppliers, R&D partners, potential clients, and investors. This understanding of innovation management follows the specifications of innovation management systems provided by the European Committee for Standardisation in CEN/TS 16555-1.

The TwinVECTOR project aims to create a centre of excellence at the Tomas Bata University in Zlín TBU, focusing on next generation battery sustainable design, energy business models, and sustainability assessments, with the support of upgraded research and administration unit (RAU). The RAU therefore coordinates the capacity building measures of the partners´ activities to emphasise the synergy and the creation of the centre of excellence at TBU. Hence, TBU will team up with excellent foreign institutions: VTT, AIT, KIT, and BAYFOR. The whole spectrum of activities is planned to activate knowledge at TBU, set-up knowledge pool and capacity building activities enabling flexible, multidisciplinary project teams to address the topic of the next generation of batteries with the help of life cycle thinking via sustainability assessments. Additionally, advanced battery technologies also need to be assessed via a combination of techno-economic simulation tools, cost-benefit analysis, and business model innovation. The widening country of Czechia, specifically the Zlín region, aims to increase scientific expertise and capacity in these areas and methods. The consortium members will share the expertise so that TBU can boost the research capacity to undertake world-class R&D activities in the energy storage field and bring them to the market. The ability to produce original ideas will be reflected in multiple outcomes expected in the short-term horizon: EU projects submitted in cooperation with excellent partners, scientific papers, conferences, and business agreements. High-impact research is expected long-term with technology transfer into practice. The existing research capacity of all members will be strengthened via additional capacity-building activities in partnership with BAYFOR.

Many aspects of our life, but also cutting-edge research questions, hinge on the solution of large systems of partial differential equations expressing conservation laws. Such equations are solved to compute accurate weather forecast, complex earthquake physics, hematic flows in patients, or the most catastrophic events in the universe. Yet, our ability to exploit the predictive power of these models is still severely limited by the computational costs of their solution. Thus, the simulation of earthquakes and their induced hazards is not yet accurate enough to prevent human losses. And our ability to model astrophysical objects is still insufficient to explain our observations. While exascale supercomputers promise the performance to tackle such problems, current numerical methods are either too expensive, because not sufficiently accurate, or too inefficient, because unable to exploit the latest supercomputing hardware. Exascale software needs to be redesigned to meet the disruptive hardware changes caused by severe constraints in energy consumption. We thus develop a new exascale hyperbolic simulation engine based on high-order communication-avoiding Finite-Volume/Discontinuous-Galerkin schemes yielding high computational efficiency. We utilize structured, spacetree grids that offer dynamic adaptivity in space and time at low memory footprint. And we consequently optimise all compute kernels to minimise energy consumption and exploit inherent fault-tolerance properties of the numerical method. As a general hyperbolic solver, the exascale engine will drive research in diverse areas and relieve scientist from the burden of developing robust and efficient exascale codes. Its development is driven by precise scientific goals, addressing grand challenges in geo- and astrophysics, such as the dynamic rupture processes and subsequent regional seismic wave propagation, or the modeling of relativistic plasmas in the collision of compact stars and explosive phenomena.