Search for: All records

The explosive growth in supercomputers capacity has changed simulation paradigms. Simulations have shifted from a few lengthy ones to an ensemble of multiple simulations with varying initial conditions or input parameters. Thus, an ensemble consists of large volumes of multi-dimensional data that could go beyond the exascale boundaries. However, the disparity in growth rates between storage capabilities and computing resources results in I/O bottlenecks. This makes it impractical to utilize conventional postprocessing and visualization tools for analyzing such massive simulation ensembles. In situ visualization approaches alleviate I/O constraints by saving predetermined visualizations in image databases during simulation. Nevertheless, the unavailability of output raw data restricts the flexibility of post hoc exploration of in situ approaches. Much research has been conducted to mitigate this limitation, but it falls short when it comes to simultaneously exploring and analyzing parameter and ensemble spaces. In this paper, we propose an expert-in-the-loop visual exploration analytic approach. The proposed approach leverages: feature extraction, deep learning, and human expert–AI collaboration techniques to explore and analyze image-based ensembles. Our approach utilizes local features and deep learning techniques to learn the image features of ensemble members. The extracted features are then combined with simulation input parameters and fed to the visualization pipeline for in-depth exploration and analysis using human expert + AI interaction techniques. We show the effectiveness of our approach using several scientific simulation ensembles. 

This Innovative Practice Work-in-Progress paper aims to capture a unique attempt to break down silos between two pre-college STEM initiatives. A myriad of programs has emerged to provide pre-college students with engineering or robotics experiences. Such initiatives are typically undertaken independent of one another. Engineering For Us All (e4usa) and For Inspiration and Recognition of Science and Technology (FIRST) are two such programs designed to excite youth about STEM careers, specifically engineering. One provides a classroom experience, while the other is primarily extracurricular, affording informal learning experiences. The parallel missions of these two programs provided the impetus for a new partnership, e4usa+FIRST, to leverage the collective strengths of each program and expand engineering access to underserved schools. A workshop was conducted that brought together a variety of stakeholders to explore numerous approaches of blending the two programs. This paper details the design of the workshop and the five emergent blending models. The results advance an argument for the involvement of all stakeholders to create an ecosystem at the pre-college level to broaden participation in engineering education. The study has the potential to impact future motivation and design of pre-college STEM education and outreach programs. 

A significant challenge on an exascale computer is the speed at which we compute results exceeds by many orders of magnitude the speed at which we save these results. Therefore the Exascale Computing Project (ECP) ALPINE project focuses on providing exascale-ready visualization solutions including in situ processing. In situ visualization and analysis runs as the simulation is run, on simulations results are they are generated avoiding the need to save entire simulations to storage for later analysis. The ALPINE project made post hoc visualization tools, ParaView and VisIt, exascale ready and developed in situ algorithms and infrastructures. The suite of ALPINE algorithms developed under ECP includes novel approaches to enable automated data analysis and visualization to focus on the most important aspects of the simulation. Many of the algorithms also provide data reduction benefits to meet the I/O challenges at exascale. ALPINE developed a new lightweight in situ infrastructure, Ascent. 

The bandgap of wurzite ZnO layers grown on 2 inch diameter c-Al2O3 substrates by pulsed laser deposition was engineered from 3.7 to 4.8 eV by alloying with Mg. Above this Mg content the layers transformed from single phase hcp to mixed hcp/fcc phase before becoming single phase fcc above a bandgap of about 5.5 eV. Metal-Semiconductor-Metal (MSM) photodetectors based on gold Inter-Digitated-Transducer structures were fabricated from the single phase hcp layers by single step negative photolithography and then packaged in TO5 cans. The devices gave over 6 orders of magnitude of separation between dark and light signal with solar rejection ratios (I270 : I350) of over 3 x 105 and dark signals of 300 pA (at a bias of -5V). Spectral responsivities were engineered to fit the “Deutscher Verein des Gas- und Wasserfaches” industry standard form and gave over two decade higher responsivities (14 A/W, peaked at 270 nm) than commercial SiC based devices. Homogeneous Ga2O3 layers were also grown on 2 inch diameter c-Al2O3 substrates by PLD. Optical transmission spectra were coherent with a bandgap that increased from 4.9 to 5.4 eV when film thickness was decreased from 825 to 145 nm. X-ray diffraction revealed that the films were of the β-Ga2O3 (monoclinic) polytype with strong (-201) orientation. β-Ga2O3 MSM photodetectors gave over 4 orders of magnitude of separation between dark and light signal (at -5V bias) with dark currents of 250 pA and spectral responsivities of up to 40 A/W (at -0.75V bias). It was found that the spectral responsivity peak position could be decreased from 250 to 230 nm by reducing film thickness from 825 to 145 nm. This shift in peak responsivity wavelength with film thickness (a) was coherent with the apparent bandgap shift that was observed in transmission spectroscopy for the same layers and (b) conveniently provides a coverage of the spectral region in which MgZnO layers show fcc/hcp phase mixing. 

β-Ga2O3 is emerging as an interesting wide band gap semiconductor for solar blind photo detectors (SBPD) and high power field effect transistors (FET) because of its outstanding material properties including an extremely wide bandgap (Eg ~4.9eV) and a high breakdown field (8 MV/cm). This review summarizes recent trends and progress in the growth/doping of β-Ga2O3 thin films and then offers an overview of the state-of-the-art in SBPD and FET devices. The present challenges for β-Ga2O3 devices to penetrate the market in real-world applications are also considered, along with paths for future work. 

The term “in situ processing” has evolved over the last decade to mean both a specific strategy for visualizing and analyzing data and an umbrella term for a processing paradigm. The resulting confusion makes it difficult for visualization and analysis scientists to communicate with each other and with their stakeholders. To address this problem, a group of over 50 experts convened with the goal of standardizing terminology. This paper summarizes their findings and proposes a new terminology for describing in situ systems. An important finding from this group was that in situ systems are best described via multiple, distinct axes: integration type, proximity, access, division of execution, operation controls, and output type. This paper discusses these axes, evaluates existing systems within the axes, and explores how currently used terms relate to the axes.