Browsing by Author "Reich, Sebastian"
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- ItemData assimilation: mathematics for merging models and data(Oberwolfach : Mathematisches Forschungsinstitut Oberwolfach gGmbH, 2018) Morzfeld, Matthias; Reich, SebastianWhen you describe a physical process, for example, the weather on Earth, or an engineered system, such as a self-driving car, you typically have two sources of information. The first is a mathematical model, and the second is information obtained by collecting data. To make the best predictions for the weather, or most effectively operate the self-driving car, you want to use both sources of information. Data assimilation describes the mathematical, numerical and computational framework for doing just that.
- ItemMathematical and Algorithmic Aspects of Atmosphere-Ocean Data Assimilation(Zürich : EMS Publ. House, 2012) Reich, Sebastian; Roulstone, Ian; Stuart, AndrewThe nomenclature “data assimilation” arises from applications in the geosciences where complex mathematical models are interfaced with observational data in order to improve model forecasts. Mathematically, data assimilation is closely related to filtering and smoothing on the one hand and inverse problems and statistical inference on the other. Key challenges of data assimilation arise from the high-dimensionality of the underlying models, combined with systematic spatio-temporal model errors, pure model uncertainty quantifications and relatively sparse observation networks. Advances in the field of data assimilation will require combination of a broad range of mathematical techniques from differential equations, statistics, probability, scientific computing and mathematical modelling, together with insights from practitioners in the field. The workshop brought together a collection of scientists representing this broad spectrum of research strands.
- ItemMathematical and Algorithmic Aspects of Data Assimilation in the Geosciences(Zürich : EMS Publ. House, 2016) Reich, Sebastian; Roulstone, Ian; Stuart, AndrewThe field of “Data Assimilation” has been driven by applications from the geosciences where complex mathematical models are interfaced with observational data in order to improve model forecasts. Mathematically, data assimilation is closely related to filtering and smoothing on the one hand and inverse problems and statistical inference on the other. Key challenges of data assimilation arise from the high-dimensionality of the underlying models, combined with systematic spatio-temporal model errors, pure model uncertainty quantification and relatively sparse observation networks. Advances in the field of data assimilation will require combination of a broad range of mathematical techniques from differential equations, statistics, machine learning, probability, scientific computing and mathematical modeling, together with insights from practitioners in the field. The workshop brought together a collection of scientists representing this broad spectrum of research strands.