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-informed learning) with hard physical constraints (Navier–Stokes in spectral space) we will develop methods to super-augment experimental data via data assimilation and turn sparse wind-tunnel measurements
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scientific machine learning-based strategies for the discovery of self-similarity laws, use of quantised local reduced order models, and real data assimilation. You will be assimilated, jointly
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methods for data assimilation; and graph-based multi-scale neural network models. While the developed methods will be broadly applicable, particular emphasis will be put on the problem of inferring gas
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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The evaluation of the ability and potential to assimilate the education will primarily be based on: Knowledge and skills relevant to doctoral studies within the research area. Ability to formulate and solve
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at the latest 29 October 2025. Your application must contain the following documents written in English (preferably) or Swedish: A CV with information on education, a list of previous and current employments, and