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    Kernel-bases Methods in Control and Estimation
    The proposed programme will establish a Doctoral Network in Mathematics with a special focus on Kernel-Based Methods in Control and Estimation. The network aims at advancing the theoretical foundations and practical applications of kernel-based techniques in control and estimation of complex dynamical systems. By bringing together leading researchers and institutions, the project will foster interdisciplinary collaboration and innovation in areas such as state estimation for partial differential equations, data-driven analysis of nonlinear dynamics, and uncertainty quantification in complex systems. The network will also explore the integration of machine learning methods, including physics-informed deep learning and Gaussian process online learning, to enhance control strategies and state estimation in uncertain and nonlinear environments. Additionally, the project will address real-world applications from different technological domains. Furthermore, by incorporating physical prior-knowledge into data-driven control and developing localized sensitivities for efficient learning, the network seeks to create robust, reliable, and efficient methods applicable across various industries. The doctoral network will not only contribute to advancing mathematical knowledge but also strengthen European innovation capacity by training highly skilled researchers equipped to tackle complex mathematical problems. Through collaborative research projects, workshops, and dissemination of findings in top-tier journals and conferences, the network will have a lasting impact on both academia and industry, fostering sustainable development and technological advancement.
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    How much water evaporates from the world’s rivers and streams? Mechanistic modeling of evaporative losses and ecological consequences
    The global network of rivers and streams spans over 80 million kilometers and is the key to human settlement, supporting diverse aquatic ecosystems, and transporting sediment and carbon into the oceans. Evaporative losses from these important aquatic arteries affect not only water availability but may influence chemical and biological processes of river ecosystems through changes in energy budget and water temperature. Despite progress in quantifying evaporation from other terrestrial compartments, estimates of evaporation from rivers remain largely local and empirical lacking predictability to varying fluvial and climatic conditions at different scales. Specifically, effects of turbulence and mixing of these dynamic evaporating surfaces, aerodynamic interactions with overlying air, variable radiative regimes and flow characteristics have not been systematically incorporated into evaporation models across a hierarchy of scales. In contrast with Penman-type approaches that build on empirical surface exchange coefficients, this project seeks to develop a physically-based framework for constraining river and stream evaporation considering a scale-appropriate energy partitioning scheme. A model of a characteristic river/stream profile (1-D) will be developed to include salient features of mixing and turbulence while accounting for variations in radiative energy inputs and flow characteristics. We hypothesize that the ingredients included in this simplified representation capture the primary processes affecting evaporative losses and water temperatures in a scalable manner while reducing empiricism. Our specific objectives are to: i) develop a mechanistic framework that incorporates hydro-aerodynamic characteristics with radiative energy partitioning and heat budget of characteristic river profiles to quantify evaporative losses over stream stretches, ii) conduct systematic laboratory and field experiments to identify dominant fluvial characteristics and environmental factors that govern evaporation dynamics to guide model evaluation and upscaling, and iii) classify spatio-temporal characteristics of global fluvial network to guide upscaling of the theoretical approach. The project will harness laboratory and field equipment and expertise at Desert Research Institute (USA), Jülich Research Centre (Germany), Utrecht University (Netherlands), and Boston University (USA). The quantitative framework will enable assessment of future climate effects on stream characteristics (early snowmelt, increasing air temperature, changes in rainfall) with important ecological consequences. The project will provide improved estimates of evaporative losses from global fluvial network towards addressing knowledge gaps concerning water balance and availability in arid regions; and improving the science underlying regional and transboundary water transfer and water rights.
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