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Uncertainty quantification (UQ) in scientific machine learning (SciML) becomes increasingly critical as neural networks (NNs) are being widely adopted in addressing complex problems across various scientific disciplines.
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Bayesian analysis of input uncertainty in hydrological modeling: 2. application
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A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data
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