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Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3148401
Abstract: Recent research to solve the parametric partial differential equations shifted the focus of conventional neural networks from finite-dimensional Euclidean space to generalized functional spaces. Neural operators learn the generalized function mapping directly, which was achieved…
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Keywords:
partial differential;
spatio spectral;
spectral neural;
neural operator ... See more keywords
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Published in 2022 at "IEEE Transactions on Geoscience and Remote Sensing"
DOI: 10.1109/tgrs.2022.3222507
Abstract: The efficiency of solving geophysical inverse problem largely relies on the efficiency of solving the corresponding forward problem. As for electromagnetic (EM) data forward modeling in frequency domain, the conventional numerical methods, e.g., finite difference…
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Keywords:
problem;
frequency domain;
operator;
neural operator ... See more keywords
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Published in 2023 at "Algorithms"
DOI: 10.3390/a16010024
Abstract: Machine Learning (ML) and/or Deep Learning (DL) methods can be used to predict fluid flow in porous media, as a suitable replacement for classical numerical approaches. Such data-driven approaches attempt to learn mappings between finite-dimensional…
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Keywords:
fourier neural;
neural operator;
fluid flow;
porous media ... See more keywords
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Published in 2023 at "Algorithms"
DOI: 10.3390/a16020124
Abstract: Simulation tools for photoacoustic wave propagation have played a key role in advancing photoacoustic imaging by providing quantitative and qualitative insights into parameters affecting image quality. Classical methods for numerically solving the photoacoustic wave equation…
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Keywords:
network;
photoacoustic wave;
fourier neural;
computational grid ... See more keywords