Final Technical Report: Probabilistic Learning, Dimension Reduction, and Spectral Representations for High-Dimensional Uncertainty Quantification
Final Technical Report
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Final Technical Report
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UQ is traditionally defined as quantitative characterization and estimation of uncertainties in both computational and real-world applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known.
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In a 2025 SAND report by Carnes et al., it was observed that experimental and model results were not in agreement, especially peak voltage, and time of peak, for gold, with observed disagreements of up to 40%. In this work, we investigate whether other sources of uncertainty in the model can explain this disagreement between experiment and model.
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Vaccines have historically played a pivotal role in controlling epidemics. Effective vaccines for viruses causing significant human disease, e.g., Ebola, Lassa fever, or Crimean Congo hemorrhagic fever virus, would be invaluable to public health strategies and counter-measure development missions. Here, we propose coverage metrics to quantify vaccine-induced CD8 + T cell-mediated immune protection, as well as metrics to characterize immuno-dominant epitopes, in light of human genetic heterogeneity and viral evolution. Proof-of-principle of our approach and methods are demonstrated for Ebola virus, SARS-CoV-2, and Burkholderia pseudomallei (vaccine) proteins.