The main objective of AERIAL-CORE is the development of core technology modules and an integrated aerial cognitive robotic system that will have unprecedented capabilities on the operational range and safety in the interaction with people, or Aerial Co-Workers (ACW), for applications such as the inspection and maintenance of large infrastructures. The project will integrate aerial robots with different characteristics to meet the requirements of: (1) Long range (several kilometres) and local very accurate (subcentimetre) inspection of the infrastructure capability; (2) Maintenance activities based on aerial manipulation involving force interactions; and (3) Aerial co-working safely and efficiently helping human workers in inspection and maintenance. AERIAL-CORE technology modules will be based on Cognitive Mechatronics and apply cognitive capabilities to aerial morphing in order to combine long range endurance and hovering for local observations, manipulation involving force interactions, and co-working with humans. The project will develop: (1) Cognitive functionalities for aerial robots including perception based on novel sensors, such as event cameras, and data fusion techniques, learning, reactivity, fast on-line planning, and teaming; (2) Aerial platforms with morphing capabilities, to save energy in long range flights and perform a very accurate inspection; (3) Cognitive aerial manipulation capabilities, including manipulation while flying, while holding with one limb, and while hanging or perching to improve accuracy and develop greater forces; (4) Cognitive safe aerial robotic co-workers capable of physical interaction with people; and (5) Integrated aerial robotic system for the inspection and maintenance of large infrastructures. The system will be demonstrated in electrical power system inspection and maintenance, which is an application with a huge economic impact that also has implications in the safety of workers and in wildlife conservation.

Climate change is one of the most urgent problems facing mankind. Implementation of the Paris climate agreement relies on robust scientific evidence. Yet, the uncertainty of non-greenhouse gas forcing associated with aerosol-cloud interactions limits our constraints on climate sensitivity. Radically new ideas are required. While the majority of forcing estimates are model based, model uncertainties remain too large to achieve the required uncertainty reductions. The quantification of aerosol cloud climate interactions in Earth Observations is thus one of the major challenges of climate science. Progress has been hampered by the difficulty to disentangle aerosol effects on clouds and climate from their covariability with confounding factors, limitations in remote sensing, very low signal-to-noise ratios as well as computationally, due to the scale of the big (>100Tb) datasets and their heterogeneity. Such big data challenges are not unique to climate science but occur across a wide range of data science applications. Innovative techniques developed by the AI and machine learning community show huge potential but have not yet found their way into climate sciences – and climate scientists are currently not trained to capitalise on these advances. The central hypothesis of IMIRACLI is that merging machine learning and climate science will provide a breakthrough in the exploration of existing datasets, and hence advance our understanding of aerosol-cloud forcing and climate sensitivity. Its innovative training plan will match each ESR with supervisors from climate and data sciences as well as a non-academic advisor and secondment and provide them with state-of-the-art data and climate science training. Partners from the non-academic sector will be closely involved in each of the projects and provide training in a commercial context. This ETN will produce a new generation of climate data scientists, ideally trained for employment in the academic and commercial sectors.

Concentrations of both greenhouse gases (GHG) and aerosols (tiny particles suspended in the atmosphere) have increased considerably since pre-industrial time. Whilst anthropogenic emissions of GHG warm the planet, aerosol emissions exert a significant, yet poorly quantified cooling that acts to offset a significant fraction of global warming from GHG. Despite decades of research, the Intergovernmental Panel on Climate Change Assessment Report continues to highlight the climate sensitivity and aerosol-cloud-interactions (ACI) as the two key uncertainties limiting our understanding of climate change. Improving model estimates of climate change sensitivity (global temperature change per unit climate forcing) to greenhouse gas emissions is primarily driven by inter-model differences how climate models represent the impacts of feedbacks between low-level clouds and the climate system as temperature increases. Reducing these inter-model differences is severely hampered by the accuracy by which low level marine boundary layer (MBL) clouds, key modulators of the net radiation budget, are represented in the Earth System Models (ESMs) we use to provide estimates of future climate scenarios. Due to computational limitations these ESMs cannot explicitly represent small-scale atmospheric processes key for the formation of MBL at the scale at which they occur in nature (down to the size of aerosols). Instead, atmospheric physical processes related to cloud formation have to be parameterised (a simplified form of the complex process). Creating simplified representations of complex cloud processes that occur over a wide range of temporal/spatial scales is a challenging undertaking for climate scientists. Uncertainties in these parameterisations propagates through to our ability to accurately represent MBL in ESMs. The focus of this project will be to improve understanding of small-scale MBL processes by addressing current deficiencies in ESM parameterisations of cloud droplet formation, the direct microphysical link between aerosols and clouds. This will be achieved by using new modelling frameworks to capitalise on detailed flight measurements of MBL clouds from the NASA Earth Venture Suborbital mission called ACTIVATE (Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment). ACTIVATE represents a novel measurement campaign of unprecedented scope for understanding MBL clouds as it will involve the deployment of two aircraft with well-matched groundspeeds. This strategy will allow for co-location of radiative properties of clouds from an aircraft flying above the MBL with an aircraft performing in-situ aerosol and cloud measurements within the MBL. This will provide a unique dataset with which we can constrain both process-scale cloud models, and large-scale ESMs to improve current small-scale ACI parameterisations, and subsequently the accuracy by which MBL clouds are represented in ESMs. To reach these goals the CLOSURE will use a new modelling framework in which a computationally fast cloud model known as a cloud parcel model (CPM). has been embedded in an ESM for the first time. These types of cloud models can accurately simulate the growth of a population of aerosol particles into cloud droplets in an ascending parcel of air. This embedded CPM framework will crucially allow for a detailed investigation of ACI in ESMs against measurements from ACTIVATE by providing additional model information for evaluation, e.g. droplet spectra. Furthermore, it will provide an efficient and seamless integration of process knowledge gained at the process scale from offline simulation to the large-scale when embedded in the ESM. This will be used to provide better understanding on the role of key small-scale processes involved in ACI for the representation of MBL clouds. The resulting improved theoretical descriptions of MBL cloud processes will reduce current uncertainties in future climate scenarios estimates.

The proposed research will provide the first proof-of-principle for a new family of Compressed Quantitative Magnetic Resonance Imaging (CQ-MRI), able to rapidly acquire a multitude of physical parameter maps for the imaged tissue from a single scan. MRI is the pre-eminent imaging modality in clinical medicine and neuroscience, providing valuable anatomical and diagnostic information. However, the vast majority of MR imaging is essentially qualitative in nature providing a `picture' of the tissue while not directly measuring its physical parameters. In contrast, quantitative MRI aims to measure properties that are intrinsic to the tissue type and independent of the scanner and scanning protocol. Unfortunately, due to excessively long scan times, Quantitative MRI is not usually included in standard protocols. The proposed research is based on a combination of a new acquisition philosophy for Quantitative MRI, called Magnetic Resonance Fingerprinting, and recent advances in model-based compressed sensing theory to enable rapid simultaneous acquisition of the multiple parameter maps. The ultimate goal of the research will be to produce a full CQ-MRI scan capability with a scan time not substantially longer than is currently needed for a standard MRI scan.

Waves, as privileged carriers of information, are an inextricable part of our world, so that controlling their propagation is a crucial stake. Scientists interest for wave-matter interactions then rose, resulting in devising two sorts of composite media: wavelength (L) scaled photonic crystals (PC) based on wave-structure interactions and deep sub-L metamaterials (MM) focusing on wave-composition ones. Due to their typically different spatial scales, mechanisms of wave propagation in PC and MM were considered distinct. Notably, opposite to PC, MM’s spatial structure is always neglected by standard homogenization approaches. Yet, using a pioneering approach, I recently evidenced the significant role of multiple scattering at sub-L scales in locally resonant MM. This led to defining the novel concept of metamaterial crystals (MMCs): resonant metamaterials with sub-L crystalline structures for which macroscopic properties stem from both resonant composition and spatial structure, opening new perspectives for sub-L wave propagation control. Perhaps one of the most exciting crystalline-driven effect that can be exploited is topological order due to the possibility to achieve backscattering-immune, robust, or non-reciprocal waveguiding. Yet most of previous proposals are based on L-scaled PC. Hence, achieving topological phases at sub-L scales and the associated new physics is still left widely unexplored, due to the largely underestimated role of multiple scattering in MMCs. Following my previous work, the ToPSeCRET project aims at investigating TOpological Properties of Sub-L scaled CRystalline mETamaterials while (i) providing new theoretical tools as well as an innovative deep physical understanding of topological non-trivial properties in terms of wave-matter interactions and (ii) designing a new class of sub-L topological MMC that will be implemented using different wave platforms (from microwaves to elastic waves).