Center for Informatics and Computational Science

Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

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Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs

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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

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Gaussian processes with PyTorch

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Uncertainty Quantification of RANS Data-Driven Turbulence Modeling

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