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We propose a novel \textit{capsule} based deep encoder-decoder model for surrogate modeling and uncertainty quantification of systems in mechanics from sparse data.
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Wei-Liem Loh · 1996
Earlier work this paper cites.
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Christopher Z Mooney · 1997
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Earlier work this paper cites.
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