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Activity Number:
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11
- Spatio-Temporal Statistical Applications
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Type:
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Invited
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Date/Time:
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Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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Council of Chapters
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Abstract #316694
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Title:
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On Bayesian Learning Frameworks in Spatial-Temporal Mechanistic Systems: Viral Pandemics and Beyond
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Author(s):
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Sudipto Banerjee* and Ian Frankenburg
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Companies:
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University of California Los Angeles and University of California Los Angeles
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Keywords:
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Bayesian inference;
Environmental processes;
Mechanistic systems;
Space-time gradients;
Uncertainty quantification;
Viral pandemics
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Abstract:
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Machine learning and high performance computing have unfurled conundrums surrounding the role of mechanistic modeling in scientific inference. Mechanistic systems refer to models built from scientific principles and laws that help understand complex mechanisms posited to be generating observational data. While machine learning algorithms often seek to circumvent the complex mechanisms through the use of large-scale datasets, mechanistic models attempt to directly relate the data to the laws of basic science. These two modeling paradigms often tend to operate exclusive to each other. We will discuss approaches for integrating these two apparently antagonistic paradigms into a single comprehensive inferential framework. Building upon developments in Bayesian inferential frameworks for spatial-temporal mechanistic models, we will arrive at valid stochastic dynamical systems engendering full probabilistic uncertainty quantification. We will focus upon systems of space-time differential equations and exploit space-time gradients to derive numerically robust learning frameworks. We will illustrate with systems designed to emulate viral pandemics as well as other environmental processes.
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Authors who are presenting talks have a * after their name.
