Research
My doctoral research at ETH Zurich's Institute for Atmospheric and Climate Science (IAC) is supervised by Ulrike Lohmann and Joel Oskarsson (ETH AI Center). At its core is the question of how scientific machine learning can augment costly physical simulations of the ocean's response to tropical cyclones. Models augmented in this way can help bring ocean hazards into global risk assessments, reliably and at a feasible computational cost. My work will integrate these models into climate adaptation and risk assessment frameworks such as the CLIMADA platform.
Publications
Research contributions
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2023
Direct numerical simulation of the distribution of floating microplastic particles in an open channel flow
Applied Research 3(1), e202200092. Acknowledged contribution: DNS and particle analysis (RUSEKU, TU Munich)
Datasets
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2026
Hydrodynamic model of the Hamburg Elbe Estuary: MIKE21 model setup, outputs and observation data
WaterBench series, published by DHI. Zenodo
Code
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2023
SPyH: A two-dimensional weakly compressible SPH fluid solver in pure NumPy
GitHub repository
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2024
tide_analytics: A Python tool for analyzing tidal characteristics from time-series data
GitHub repository
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2024
sns_autocalibration: Autocalibration of a MIKE FM Southern North Sea model
GitHub repository