Computational Methods in Ice-sheet Modeling - Uncertainty Quantification and Optimization
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it 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.