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Computational Methods in Ice-sheet Modeling - Uncertainty Quantification and Optimization

Offered By: Society for Industrial and Applied Mathematics via YouTube

Tags

Bayesian Inference Courses Partial Differential Equations Courses Sea-Level Rise Courses Greenland Ice Sheet Courses Uncertainty Quantification Courses

Course Description

Overview

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Explore computational methods for ice-sheet modeling in this webinar from the SIAM Activity Group on Mathematics of Planet Earth. Delve into state-of-the-art techniques for calibrating Greenland and Antarctic ice sheet models through high-dimensional parameter inversion. Learn about the critical role of ice sheet mass loss in global sea level rise and the importance of accurate modeling for future projections. Discover how large-scale PDE-constrained optimization and Bayesian inference are applied to approximate parameter distributions efficiently. Examine a case study on the Humboldt Glacier in Greenland, focusing on how basal friction parameter uncertainties affect mass loss predictions. Gain insights into multi-fidelity methods that significantly reduce computational costs in estimating glacier mass change statistics. The webinar concludes with a Q&A session, offering further exploration of applied mathematics in environmental science, sustainability, and climate policy.

Syllabus

Introduction
Webinar
Q&A


Taught by

Society for Industrial and Applied Mathematics

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