Verification Validation and Uncertainty Quantification Supporting Simulation and Analysis Development.
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This paper considers calculation of prediction intervals (PIs) by neural networks (NNs) for quantifying uncertainty in regression tasks, so as to provide fast, accurate and robust emulators to accelerate scientific simulations. We propose a novel method to learn lower and upper bounds of the PI using independent NNs without defining an exclusive loss. Our method requires no distributional assumption, does not introduce extra hyper-parameters, and can effectively identify out-of-distribution samples and quantify their uncertainty. We demonstrate advantages of our method using a benchmark problem and two real-world scientific applications.
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A method for automated microbial detection includes collecting air particles into a solid-state sampler, the air particles including microbes, charging the air particles using a plasma field generated by propulsion electrodes, focusing the charged air particles toward a sample well of a microfluidic testing cartridge, tagging the charged air particles with a fluorescence marker, and detecting a quantity of the microbes using a fluorescence detector.
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Presentation to be presented during the Site-Directed Research and Development (SDRD) Program FY 2021 Review Meeting (webex), September 22–23, 2021.
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