The WeForming project aims to change the paradigm of efficiently managing energy in buildings, paying special attention to their interaction with the energy ecosystem (energy networks and markets) by developing, deploying and demonstrating innovative solutions addressing (i) digital operation, management and maintenance and (ii) efficient and interactive energy processing for Intelligent Grid-interactive Efficient Buildings (iGEBs), able to operate intelligently in a multi-energy, multi-user, multi-sector, multi-market, and multi-objective environment, without forgetting quality, comfort, health and acceptation. The WeForming Framework will deploy an umbrella framework covering all aspects around the establishment and adoption of iGEBs in cities, which consists of (i) a Building Operational Pillar encompassing all assets of iGEBs, including the energy management systems and the different platforms, aiming towards the building operational optimization, the actors and the actual flexibility resources, generation and storage components; (ii) a novel Interoperability Assurance Pillar which leverages on, adapts, evolves, and specifically validates leading-edge interoperable architectures; (iii) a Business Pillar including all necessary processes to bring the proposed solutions to the market, through the design and validation of sustainable and competitive business models, ensuring the economic viability of the investments needed for the establishment of smart cities featuring new and refurbished iGEBs; (iv) a Smart-city enabling Pillar integrating iGEBs as modular units addressing all different non-technical barriers and limitations for the widespread deployment of the proposed solutions that will create a stock of active buildings acting as active utility nodes within cities.

Earth-Moving, Forestry, and Urban Logistics are sectors where increased autonomy can spur drastic economic growth along with addressing some core societal (e.g. address labor shortage) and environmental problems (e.g. minimize soil damage, fuel consumption). Yet there are persisting challenges related to variations of tasks/environments that are intricately linked to the terrain-machine contact encountered during navigation and manipulation. For example, an Excavator machine used in Earth-Moving needs to adapt to different types of terrain (ground) underneath (loose soil, rocks of different shapes and sizes), for scooping. Such task and environment adaptation require machines to modify their “perception-to-action” mapping based on online observations from different sensing modalities. XSCAVE will leverage the exceptional representation and approximation capabilities of deep neural networks to automatically learn the terrain/specific adaptation of excavation, forwarding, and navigation strategies from data. The overall objective of XSCAVE is (i) to develop capabilities for learning performant (high-speed), safe (stable, contact-aware), and explainable perception-to-action models for terrain adaptive excavation and navigation strategies and (ii) demonstrate step-change in autonomy for Excavation, Forwarding and Navigation tasks prevalent in Earth-Moving, Forestry and Logistics industries. To this end, XSCAVE aims to re-imagine deep-learned models as neural networks augmented with parameterized structured priors derived from physics, optimization, and classical search to bring domain knowledge into the learning pipeline. The fundamental innovations at the algorithmic level will translate to unprecedented ability for the machines to plan, control and adapt their actions depending on the task and terrain contact conditions. The end-results will be demonstrated in partnership with Novatron (earth-moving), Komatsu (forestry), and Clevon (outdoor logistic vehicles).

Five leading European supercomputing centres are committed to develop, within their respective national programs and service portfolios, a set of services that will be federated across a consortium. The work will be undertaken by the following supercomputing centres, which form the High Performance Analytics and Computing (HPAC) Platform of the Human Brain Project (HBP): ▪ Barcelona Supercomputing Centre (BSC) in Spain, ▪ The Italian supercomputing centre CINECA, ▪ The Swiss National Supercomputing Centre CSCS, ▪ The Jülich Supercomputing Centre in Germany, and ▪ Commissariat à l'énergie atomique et aux énergies alternatives (CEA), France (joining in April 2018). The new consortium will be called Fenix and it aims at providing scalable compute and data services in a federated manner. The neuroscience community is of particular interest in this context and the HBP represents a prioritised driver for the Fenix infrastructure design and implementation. The Interactive Computing E-Infrastructure for the HBP (ICEI) project will realise key elements of this Fenix infrastructure that are targeted to meet the needs of the neuroscience community. The participating sites plan for cloud-like services that are compatible with the work cultures of scientific computing and data science. Specifically, this entails developing interactive supercomputing capabilities on the available extreme computing and data systems. Key features of the ICEI infrastructure are: ▪ Scalable compute resources; ▪ A federated data infrastructure; and ▪ Interactive Compute Services providing access to the federated data infrastructure as well as elastic access to the scalable compute resources. The ICEI e-infrastructure will be realised through a coordinated procurement of equipment and R&D services. Furthermore, significant additional parts of the infrastructure and R&D services will be realised within the ICEI project through in-kind contributions from the participating supercomputing centres.