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Polynomial Chaos Surrogate Construction for Random Fields with Parametric Uncertainty

Engineering and applied science rely on computational experiments to rigorously study physical systems. The mathematical models used to probe these systems are highly complex, and sampling-intensive studies often require prohibitively many …

A Novel Framework to Project the Permafrost Fate With Explicit Quantification of Soil Property and Future Climate Uncertainties

Abstract This study develops a novel general framework to project the permafrost fate with rigorous uncertainty quantification to assess dominant sources. Borehole temperature records from three sites in the Russian western Arctic are used to …

Bayesian Calibration of UO2 Creep Rates as a Tool for Accelerated Fuel Qualification

Mechanistic models informed by lower-length-scale simulations have a role to play in accelerating fuel qualification by enabling the use of separate effects tests to reduce uncertainty on model parameters that impact the predictions of in-reactor …

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 …

Improving the Quasi-Biennial Oscillation via a Surrogate-Accelerated Multi-Objective Optimization

Abstract Accurate simulation of the quasi-biennial oscillation (QBO) is challenging due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity …

Mechanistic Multiscale Uncertainty Propagation in Support of Accelerated Fuel Qualification

Taking a nuclear fuel concept through the research, development, and qualification stages has historically taken on the order of 20 to 25 years because of extensive irradiation tests required for a variety of conditions. The concept of …

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 …

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 …

The Energy Exascale Earth System Model Version 3: 1. Overview of the Atmospheric Component

This paper describes the atmospheric component of the US Department of Energy's Energy Exascale Earth System Model (E3SM) version 3. Significant updates have been made to the atmospheric physics compared to earlier versions. Specifically, interactive …

A Lake Biogeochemistry Model for Global Methane Emissions: Model Development, Site-Level Validation, and Global Applicability

Abstract Lakes are important sentinels of climate change and may contribute over 30% of natural methane (CH4) emissions; however, no earth system model (ESM) has represented lake CH4 dynamics. To fill this gap, we refined a process-based lake …