Assessing the impacts of droughts and heat waves at thermoelectric power plants in the United States using integrated regression, thermodynamic, and climate models
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Cook, Margaret A. (ORCID:0000000187446918)↗
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Results for “regression”
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Change point and degree day baseline regression models in industrial facilities
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Golden, Allyson↗
The concentration-response between long-term PM2.5 exposure and mortality; A meta-regression approach
Highlights: • PM2.5-mortality effect is significant below 10 μg/m3 and above 20 μg/m3. • Better exposure estimates result in higher effect size estimates. • More control for SES results in higher effect size estimates. • A nonlinear dose-response is seen. • Effects at higher exposures are larger than predicted by the IER/GBD. Long-term exposure to ambient fine particulate matter (≤ 2.5 μg/m{sup 3} in aerodynamic diameter; PM{sub 2.5}) is significantly associated with increased risk of premature mortality. Our goal was to provide an updated meta-analysis of all-cause and cause-specific mortality associated with exposure to PM{sub 2.5} and to better estimate the risk of death as a function of air pollution levels.
54 ENVIRONMENTAL SCIENCES↗
Data-driven stochastic AC-OPF using Gaussian process regression
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Mitrovic, Mile (ORCID:0000000166912772)↗
Composition-dependence of stacking fault energy in austenitic stainless steels through linear regression with random intercepts
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Meric de Bellefon, G.↗
Modeling fission gas release at the mesoscale using multiscale DenseNet regression with attention mechanism and inception blocks
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Toma, Peter (ORCID:0009000126534575)↗
Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression
Here, we use Gaussian stochastic weight averaging (SWAG) to assess the epistemic uncertainty associated with neural-network-based function approximation relevant to fluid flows. SWAG approximates a posterior Gaussian distribution of each weight, given training data, and a constant learning rate. Having access to this distribution, it is able to create multiple models with various combinations of sampled weights, which can be used to obtain ensemble predictions. The average of such an ensemble can be regarded as the 'mean estimation', whereas its standard deviation can be used to construct 'confidence intervals', which enable us to perform uncertainty quantification (UQ) with regard to the training process of neural networks. We utilize representative neural-network-based function approximation tasks for the following cases: (i) a two-dimensional circular-cylinder wake; (ii) the DayMET dataset (maximum daily temperature in North America); (iii) a three-dimensional square-cylinder wake; and (iv) urban flow, to assess the generalizability of the present idea for a wide range of complex datasets. SWAG-based UQ can be applied regardless of the network architecture, and therefore, we demonstrate the applicability of the method for two types of neural networks: (i) global field reconstruction from sparse sensors by combining convolutional neural network (CNN) and multi-layer perceptron (MLP); and (ii) far-field state estimation from sectional data with two-dimensional CNN. We find that SWAG can obtain physically-interpretable confidence-interval estimates from the perspective of epistemic uncertainty. This capability supports its use for a wide range of problems in science and engineering.
97 MATHEMATICS AND COMPUTING↗