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  1. Brehm, Christoph ; Pandya, Shishir (Ed.)
    Computational fluid dynamics (CFD) and its uncertainty quantification are computationally expensive. We use Gaussian Process (GP) methods to demonstrate that machine learning can build efficient and accurate surrogate models to replace CFD simulations with significantly reduced computational cost without compromising the physical accuracy. We also demonstrate that both epistemic uncertainty (machine learning model uncertainty) and aleatory uncertainty (randomness in the inputs of CFD) can be accommodated when the machine learning model is used to reveal fluid dynamics. The demonstration is performed by applying simulation of Hagen-Poiseuille and Womersley flows that involve spatial and spatial-tempo responses, respectively. Training points are generated by using the analytical solutions with evenly discretized spatial or spatial-temporal variables. Then GP surrogate models are built using supervised machine learning regression. The error of the GP model is quantified by the estimated epistemic uncertainty. The results are compared with those from GPU-accelerated volumetric lattice Boltzmann simulations. The results indicate that surrogate models can produce accurate fluid dynamics (without CFD simulations) with quantified uncertainty when both epistemic and aleatory uncertainties exist.
    Free, publicly-accessible full text available July 1, 2023
  2. Microwave loss in niobium metallic structures used for superconducting quantum circuits is limited by a native surface oxide layer formed over a timescale of minutes when exposed to an ambient environment. In this work, we show that nitrogen plasma treatment forms a niobium nitride layer at the metal–air interface, which prevents such oxidation. X-ray photoelectron spectroscopy confirms the doping of nitrogen more than 5 nm into the surface and a suppressed oxygen presence. This passivation remains stable after aging for 15 days in an ambient environment. Cryogenic microwave characterization shows an average filling-factor-adjusted two-level-system loss tangent [Formula: see text] of [Formula: see text] for resonators with a 3 [Formula: see text]m center strip and [Formula: see text] for a 20 [Formula: see text]m center strip, exceeding the performance of unpassivated samples by a factor of four.

    Free, publicly-accessible full text available March 11, 2023
  3. Free, publicly-accessible full text available June 1, 2023