Exploring the robustness of a surrogate-based ice flow model calibration
Thesis (M.S.) University of Alaska Fairbanks, 2023 When simulating ice sheets using numerical models, model parameters have a great impact on the ice flow, velocity, and response to external forces, making them key factors in accurately predicting ice sheet behavior. In order to have confidence in m...
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ftunivalaska:oai:scholarworks.alaska.edu:11122/14943 2024-04-28T07:55:36+00:00 Exploring the robustness of a surrogate-based ice flow model calibration Blum, Kyle Aschwanden, Andy Newman, David Truffer, Martin Wackerbauer, Renate 2023-12 http://hdl.handle.net/11122/14943 en_US eng http://hdl.handle.net/11122/14943 Department of Physics Glaciers Antarctica Glaciology Master of Science in Physics Thesis ms 2023 ftunivalaska 2024-04-03T14:16:26Z Thesis (M.S.) University of Alaska Fairbanks, 2023 When simulating ice sheets using numerical models, model parameters have a great impact on the ice flow, velocity, and response to external forces, making them key factors in accurately predicting ice sheet behavior. In order to have confidence in modeled predictions of ice sheets, we must first have confidence in the physical models we use to simulate them. In order to have confidence in these physical models, we must first have confidence in our calibrations of these ice dynamics parameters. Researchers have been training neural networks to emulate expensive ice sheet models. These surrogate ice sheet models are designed to take ice flow parameters as input, and to output ice surface speed fields that closely resemble modeled fields that physically based ice sheet models would calculate. In this way, these surrogate models act as a computationally inexpensive alternative to map model parameter values to the resulting calculated ice speeds. These surrogates have been implemented and leveraged for Bayesian statistically based approaches to ice flow parameter calibrations that would otherwise be intractable using a high fidelity ice sheet model. By examining the methods we use to train these surrogates, and the extent to which the architecture of these surrogates affect their performance, we can have more confidence in our calibrations that make use of them. Here we present an analysis of the methods with which we train surrogate models as a means of calibrating important ice flow parameters. We focus on determining desirable characteristics for data over which the neural networks are trained, as well as the architecture of the surrogates themselves. Thesis Antarc* Antarctica glaciers Ice Sheet Alaska University of Alaska: ScholarWorks@UA |
institution |
Open Polar |
collection |
University of Alaska: ScholarWorks@UA |
op_collection_id |
ftunivalaska |
language |
English |
topic |
Glaciers Antarctica Glaciology Master of Science in Physics |
spellingShingle |
Glaciers Antarctica Glaciology Master of Science in Physics Blum, Kyle Exploring the robustness of a surrogate-based ice flow model calibration |
topic_facet |
Glaciers Antarctica Glaciology Master of Science in Physics |
description |
Thesis (M.S.) University of Alaska Fairbanks, 2023 When simulating ice sheets using numerical models, model parameters have a great impact on the ice flow, velocity, and response to external forces, making them key factors in accurately predicting ice sheet behavior. In order to have confidence in modeled predictions of ice sheets, we must first have confidence in the physical models we use to simulate them. In order to have confidence in these physical models, we must first have confidence in our calibrations of these ice dynamics parameters. Researchers have been training neural networks to emulate expensive ice sheet models. These surrogate ice sheet models are designed to take ice flow parameters as input, and to output ice surface speed fields that closely resemble modeled fields that physically based ice sheet models would calculate. In this way, these surrogate models act as a computationally inexpensive alternative to map model parameter values to the resulting calculated ice speeds. These surrogates have been implemented and leveraged for Bayesian statistically based approaches to ice flow parameter calibrations that would otherwise be intractable using a high fidelity ice sheet model. By examining the methods we use to train these surrogates, and the extent to which the architecture of these surrogates affect their performance, we can have more confidence in our calibrations that make use of them. Here we present an analysis of the methods with which we train surrogate models as a means of calibrating important ice flow parameters. We focus on determining desirable characteristics for data over which the neural networks are trained, as well as the architecture of the surrogates themselves. |
author2 |
Aschwanden, Andy Newman, David Truffer, Martin Wackerbauer, Renate |
format |
Thesis |
author |
Blum, Kyle |
author_facet |
Blum, Kyle |
author_sort |
Blum, Kyle |
title |
Exploring the robustness of a surrogate-based ice flow model calibration |
title_short |
Exploring the robustness of a surrogate-based ice flow model calibration |
title_full |
Exploring the robustness of a surrogate-based ice flow model calibration |
title_fullStr |
Exploring the robustness of a surrogate-based ice flow model calibration |
title_full_unstemmed |
Exploring the robustness of a surrogate-based ice flow model calibration |
title_sort |
exploring the robustness of a surrogate-based ice flow model calibration |
publishDate |
2023 |
url |
http://hdl.handle.net/11122/14943 |
genre |
Antarc* Antarctica glaciers Ice Sheet Alaska |
genre_facet |
Antarc* Antarctica glaciers Ice Sheet Alaska |
op_relation |
http://hdl.handle.net/11122/14943 Department of Physics |
_version_ |
1797580516757602304 |