Reconciling machine learning and glacier system modelling
Within this sub-project, we envisage to combine model-driven simulations of a coupled glacier system with data-driven machine learning techniques at specific benchmark sites in the Alps (e.g., Hintereisferner, Vernagtferner). A well-established ice-flow model will first be employed to produce observation-informed training and test data, which will then inform machine learning components that can replace such classical methods. The ultimate goal is to reduces computational efforts and prepare such hybrid techniques for long timescales and/or regional scales.
Detailed Information and position requirements of this SP are formulated in this PDF.
For specific information on the sub-project please contact: Dr. Johannes Fürst (NGL)
Department of Geography and Geosciences, Institute of Geography
Wetterkreuz 15, 91058 Erlangen, T: 09131-85-26680, Johannes.fuerst@fau.de Prof. Dr. Harald Köstler
Department of Computer Sciences, System Simulation
Cauerstraße 11, 91058 Erlangen, T: 09131-85-29359, harald.koestler@fau.de
Co-PIs: I. Tabone (FAU Geography)
O. Galiardini (Univ. Grenoble)
F. Maussion (Univ. Innsbruck)
Get to know our project affiliated PhD students
Mamta KC
mamta.kc@fau.de
After graduating with a master’s degree in Environmental and Natural Resources from Kathmandu University, Nepal, I opted for the second master’s program in Climate and Environmental Science at the FAU Institute of Geography to broaden my horizon and enhance my environmental data analysis knowledge. During my master’s program, my interest in glacier environment, climatology, and machine learning grew.
The potential of deep learning applications to understand real-world physical phenomena captured my attention. Using efficient deep learning-based models to learn physics-based glacier models is a promising field of research. Thus, I look forward to incorporating machine learning into glacier modeling during my Ph.D. in the IDP M3OCCA program. My research will focus on developing physics-based deep learning emulators that will learn to predict the glacier dynamics. The emulators will be trained using the simulated data generated from the different glacier-based physical models.
Subproject 3.2
Reconciling machine learning and glacier system modelling
Within this sub-project, we envisage to combine model-driven simulations of a coupled glacier system with data-driven machine learning techniques at specific benchmark sites in the Alps (e.g., Hintereisferner, Vernagtferner). A well-established ice-flow model will first be employed to produce observation-informed training and test data, which will then inform machine learning components that can replace such classical methods. The ultimate goal is to reduces computational efforts and prepare such hybrid techniques for long timescales and/or regional scales.
Detailed Information and position requirements of this SP are formulated in this PDF.
For specific information on the sub-project please contact:
Dr. Johannes Fürst (NGL)
Department of Geography and Geosciences, Institute of Geography
Wetterkreuz 15, 91058 Erlangen, T: 09131-85-26680, Johannes.fuerst@fau.de
Prof. Dr. Harald Köstler
Department of Computer Sciences, System Simulation
Cauerstraße 11, 91058 Erlangen, T: 09131-85-29359, harald.koestler@fau.de
Co-PIs:
I. Tabone (FAU Geography)
O. Galiardini (Univ. Grenoble)
F. Maussion (Univ. Innsbruck)
Get to know our project affiliated PhD students
Mamta KC
mamta.kc@fau.de
After graduating with a master’s degree in Environmental and Natural Resources from Kathmandu University, Nepal, I opted for the second master’s program in Climate and Environmental Science at the FAU Institute of Geography to broaden my horizon and enhance my environmental data analysis knowledge. During my master’s program, my interest in glacier environment, climatology, and machine learning grew.
The potential of deep learning applications to understand real-world physical phenomena captured my attention. Using efficient deep learning-based models to learn physics-based glacier models is a promising field of research. Thus, I look forward to incorporating machine learning into glacier modeling during my Ph.D. in the IDP M3OCCA program. My research will focus on developing physics-based deep learning emulators that will learn to predict the glacier dynamics. The emulators will be trained using the simulated data generated from the different glacier-based physical models.