The content introduces a framework that combines two methods for quantifying uncertainties in computational models. It discusses the challenges of specifying Quantity of Interest (QoI) maps and presents a machine-learning-enabled process to address these challenges. The LUQ framework is detailed, emphasizing the steps involved in transforming noisy datasets into distributions for data-consistent inversion. The mathematical and algorithmic contributions are highlighted, along with numerical examples illustrating the application of the framework.
The work emphasizes the importance of filtering noisy data, learning uncertain quantities through feature extraction, and computing Data-Consistent Inversion (DCI) solutions. It extends the LUQ framework to handle spatial and spatio-temporal data using deep learning techniques. The use of neural networks and radial basis functions for filtering is discussed, providing insights into optimizing NNs for accurate approximation of underlying signals.
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by Taylor Roper... في arxiv.org 03-07-2024
https://arxiv.org/pdf/2403.03233.pdfاستفسارات أعمق