Deutsch Intern
    Data Science Chair

    ML for Ecosystems & Climate Modeling

    The Data Science Chair works with environmental data to contribute to the ongoing climate research. Based on low-cost sensors, map and geographical data, we develop novel approaches to environmental questions. These questions include "What is the air pollution at a given spot on earth?" and "What type of wine should I grow on my land given the development in climate in the next few decades?". While answering such questions is one concern of our research, the other objective is to raise awareness for the environment in the society.

    Open Topics

    11/21/2024 | Physics & Deep Learning, Ecosystems

    Spherical Embeddings or Spherical Fourier Neural Operators

    Spherical Harmonics by Inigo.quilez, licensed under CC BY-SA 3.0, via Wikimedia Commons, (https://commons.wikimedia.org/wiki/File:Spherical_Harmonics.png)
    Although our planet is a sphere and therefor all weather and climate processes operate on sphere, Deep Learning Weather Models very commonly operate on different rectangular projections of the sphere. This introduces increasingly… more
    11/13/2024 | Physics & Deep Learning, Ecosystems

    Meteorological Downscaling Using Remote Sensing Data

    Global numerical climate models project meteorological quantities such as temperature, humidity, pressure, etc. decades into the future under the assumption of different emission scenarios. However, due to computational… more
    11/11/2024 | Physics & Deep Learning, Ecosystems

    Resolution-Invariant Training of Deep Learning Athmospheric Models

    Athmosperic Circulation Models simulate the evolution of the state of the atmosphere and are used to generate weather forecasts or climate projections. Deep Learning has successfully been applied to learn the dynamics of the… more