Enhancing Energy Production with Exascale HPC Methods

High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and from the Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the Intel Corporation, which enabled us to obtain the presented experimental results in uncertainty quantification in seismic imagingPostprint (author's final draft

Context-aware visual exploration of molecular databases

Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions

StarGO: A New Method to Identify the Galactic Origins of Halo Stars

We develop a new method StarGO (Stars' Galactic Origin) to identify the galactic origins of halo stars using their kinematics. Our method is based on self-organizing map (SOM), which is one of the most popular unsupervised learning algorithms. StarGO combines SOM with a novel adaptive group identification algorithm with essentially no free parameters. In order to evaluate our model, we build a synthetic stellar halo from mergers of nine satellites in the Milky Way. We construct the mock catalogue by extracting a heliocentric volume of 10 kpc from our simulations and assigning expected observational uncertainties corresponding to bright stars from Gaia DR2 and LAMOST DR5. We compare the results from StarGO against that from a Friends-of-Friends (FoF) based method in the space of orbital energy and angular momentum. We show that StarGO is able to systematically identify more satellites and achieve higher number fraction of identified stars for most of the satellites within the extracted volume. When applied to data from Gaia DR2, StarGO will enable us to reveal the origins of the inner stellar halo in unprecedented detail.Comment: 11 pages, 7 figures, Accepted for publication in Ap