Titlebook: Computational Science and Its Applications – ICCSA 2023 Workshops; Athens, Greece, July Osvaldo Gervasi,Beniamino Murgante,Carmelo M. Torr

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Claudia E. Henninger,Pallavi Singhe. Thus, we obtain an effective way of suppressing numerical dispersion in numerically modeled seismic fields. This article discusses a method for constructing an optimal training dataset based on the properties of a velocity model. We build a distance matrix for the parts of the model that correspo

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Introduction to Sustainability in Fashion,ry. Frequency-domain processing has several advantages over time-domain processing, including more efficient extraction of information from large datasets, noise removal, and correction for amplitude and phase distortions. The proposed approach can significantly improve the exploration and character

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Tran Thi Trieu,Nguyen Lam Gianged to suppress numerical error in seismic modeling results. In this approach, a small number of seismograms generated using coarse and fine girds are used to train the network, mapping the inaccurate coarse-grid data to the high-quality fine-grid data. After that, the network is applied to the entir

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https://doi.org/10.1007/978-90-481-9914-3 makes it possible to perform such an assessment using limited computing resources, in particular, personal computers. It is based on a decomposition of the 3D sample image into small representative sub-samples, for which the absolute permeability is estimated based on the numerical solution of the