neural-networks

Uncertainty Quantification in Computational Science -- from Physical Models to Neural Networks

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Coupled Lake-Atmosphere-Land Physics Uncertainties in a Great Lakes Regional Climate Model

Abstract This study develops a surrogate-based method to assess the uncertainty within a convective permitting integrated modeling system of the Great Lakes region, arising from interacting physics parameterizations across the lake, atmosphere, and …

Surrogate Construction via Weight Parameterization of Residual Neural Networks

Surrogate model development is a critical step for uncertainty quantification or other sample-intensive tasks for complex computational models. In this work we develop a multi-output surrogate form using a class of neural networks (NNs) that employ …

Spatio-Temporal Surrogate Construction and Calibration of E3SM Land Model

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Reduced-Dimensional Neural Network Surrogate Construction for the E3SM Land Model

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Quantifying Uncertainties in Weight-Parameterized Residual Neural Networks

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Reduced-Dimensional Neural Network Surrogate Construction for the E3SM Land Model

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Reduced-Dimensional Neural Network Surrogate Construction and Calibration of the E3SM Land Model

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Importance Sampling within Configuration Space Integration for Adsorbate Thermophysical Properties: A Case Study for CH3/Ni(111)

A new strategy is presented for computing anharmonic partition functions for the motion of adsorbates relative to a catalytic surface. Importance sampling is compared with conventional Monte Carlo. The importance sampling is significantly more …

Measuring Stiffness in Residual Neural Networks

In this work, we define the concept of stiffness for residual neural networks (ResNets) relying on the fact that ResNets can be viewed as a discretization of an underlying neural ordinary differential equation (NODE). We then propose several metrics …