A Machine-Studying Enabled Framework for Quantifying Uncertainties in Parameters of Computational Fashions


[Submitted on 4 Mar 2024]

Obtain a PDF of the paper titled From Displacements to Distributions: A Machine-Studying Enabled Framework for Quantifying Uncertainties in Parameters of Computational Fashions, by Taylor Roper and Harri Hakula and Troy Butler

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Summary:This work presents novel extensions for combining two frameworks for quantifying each aleatoric (i.e., irreducible) and epistemic (i.e., reducible) sources of uncertainties within the modeling of engineered methods. The information-consistent (DC) framework poses an inverse downside and resolution for quantifying aleatoric uncertainties by way of pullback and push-forward measures for a given Amount of Curiosity (QoI) map. Sadly, a pre-specified QoI map is just not all the time accessible a priori to the gathering of knowledge related to system outputs. The information themselves are sometimes polluted with measurement errors (i.e., epistemic uncertainties), which complicates the method of specifying a helpful QoI. The Studying Unsure Portions (LUQ) framework defines a proper three-step machine-learning enabled course of for remodeling noisy datasets into samples of a realized QoI map to allow DC-based inversion. We develop a strong filtering step in LUQ that may study essentially the most helpful quantitative info current in spatio-temporal datasets. The realized QoI map transforms simulated and noticed datasets into distributions to carry out DC-based inversion. We additionally develop a DC-based inversion scheme that iterates over time as new spatial datasets are obtained and makes use of quantitative diagnostics to determine each the standard and impression of inversion at every iteration. Reproducing Kernel Hilbert House idea is leveraged to mathematically analyze the realized QoI map and develop a quantitative sufficiency check for evaluating the filtered knowledge. An illustrative instance is utilized all through whereas the ultimate two examples contain the manufacturing of shells of revolution to reveal numerous facets of the introduced frameworks.

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From: Harri Hakula [view email]
Mon, 4 Mar 2024 20:40:50 UTC (5,324 KB)

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