Soft Computing in GeoSience
Because of:
- the complexity of the underlying physical processes resulting in models with unfeasibly large computational demands
- large amounts of data
- uncertain, imprecise and sparse availability of measurement data
- still vague understanding of some of the underlying physical processes
many problems in geoscience are still intractable by conventional computational means.
In order to tackle these problems we develop soft computing methods which combine computational techniques from machine learning and numerical optimization with the knowledge base of geoscience to provide suitable solutions with justifiable computational effort.
Currently we work in cooperation with geoscientists from the TU Dresden and the University of Tübingen on:
- Neural networks for the approximation of water transport processes in the vadose zone.
- Neural networks for the approximation of tsunami inundation.
- Evolutionary Algorithms for the optimization of irrigation schedules.
- Evolutionary Algorithms for the optimization of geothermal systems.
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Overview of the FIM model-system used for the simulation of crop-growth in deficit irrigation scenarios.
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(a) optimized positioning of heat pumps for a large scale geothermal system
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Contact
Michael de Paly, Tel.: (07071)
29-78979, michael.depaly at uni-tuebingen.de
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