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Activity Number:
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261
- Statistical Learning for Environmental Data Science
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Type:
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Invited
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Date/Time:
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Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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Section on Statistics and the Environment
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Abstract #316827
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Title:
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Data-Driven Discovery of Dynamics in Environmental Systems
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Author(s):
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Christopher Wikle* and Joshua S North and Erin M Schliep
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Companies:
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University of Missouri and University of Missouri and University of Missouri
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Keywords:
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spatial;
spatio-temporal;
dynamics;
environmental
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Abstract:
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Environmental processes are governed by complex dynamical interactions across multiple scales and multiple processes. The specification of realistic models for such dynamical processes can be facilitated by mechanistically motivated models, with effective regularization. However, often the time scales or multi-process complexity of such processes preclude the effective specification of a specific mechanism by which to motivate the model. Recently, there has been interest in the applied math community to use data-driven discovery methods to learn the fundamental dynamical mechanisms present in data. These approaches have shown promise in simulations of low-dimensional systems without a great deal of noise in observation or process. Here, we present a statistical approach for dynamic discovery that accommodates noisy observations and stochastic dynamics that can be applied even with relatively short time series. The method is illustrated on simulated systems and applied to a real-world environmental process.
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Authors who are presenting talks have a * after their name.
