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Science and Information Conference (SAI)

作者： A. S. Elons Dalia Ahmed Magdi M. Y. Elgendy Scientific Computing Department Ain-Shams University Cairo Egypt Computer and Information System Department Sadat Academy for Management Sciences Cairo Egypt Research and Development Department Apex Company Cairo Egypt

ISBN: (纸本)9781467384612

In the early days Hydrocarbon wells were normally drilled vertically which rarely requires making decisions. As the technology progressed, the capability to do some deviation of the bit was used resulting in deviated or directional wells to follow the Hydrocarbon formation. This type of drilling requires real-time drilling directions decisions. In this paper, the aim is to develop a real-time intelligent system that can model and predict the optimal drilling path for a new well before actual drilling is done based on the geological layers of the pre-drilled surrounding wells. In the presented work, a hybrid method of stochastic population-based search algorithm (Particle Swarm Optimization) and a gradient based algorithm (Back Propagation) is used to train a Multiplicative Neural Network. The proposed network topology and training algorithms have shown superiority on the traditional Neural Network for determining the drilling optimal drilling path. The proposed system generated proposed drilling plans that achieved more than 88% drilling decision accuracy which is measured according to the actual drilling path.

关键词： Reservoirs Rocks Multi-layer neural network Neurons Predictive models

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Integrable discretization of soliton equations via bilinear method and Backlund transformation

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Science China Mathematics 2015年第2期58卷 279-296页

作者： ZHANG Ying Nan CHANG Xiang Ke HU Juan HU Xing Biao TAM Hon-Wah Institute of Computational Mathematics and Scientific Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Department of Applied Mathematics Zhejiang University of Technology Department of Computer Science Hong Kong Baptist University

We present a systematic procedure to derive discrete analogues of integrable PDEs via Hirota’s bilinear *** approach is mainly based on the compatibility between an integrable system and its B¨acklund *** apply this procedure to several equations,including the extended Korteweg-deVries(Kd V)equation,the extended Kadomtsev-Petviashvili(KP)equation,the extended Boussinesq equation,the extended Sawada-Kotera(SK)equation and the extended Ito equation,and obtain their associated semidiscrete *** the continuum limit,these differential-difference systems converge to their corresponding smooth *** these new integrable systems,their B¨acklund transformations and Lax pairs are derived.

关键词： integrable discretization bilinear method Backlund transformation

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Advances in Nature and Biologically Inspired computing: Proceedings of the 7th World Congress on Nature and Biologically Inspired computing (NaBIC2015) in Pietermaritzburg, South Africa, held December 01-03, 2015 7th

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7th World Congress on Nature and Biologically Inspired computing, NaBIC 2015

作者： Pillay, Nelishia Engelbrecht, Andries P. Abraham, Ajith Du Plessis, Mathys C. Snášel, Václav Muda, Azah Kamilah School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Pietermaritzburg South Africa Department of Computer Science School of Information Technology University of Pretoria Pretoria South Africa Machine Intelligence Research Labs Scientific Network for Innovation and Research Excellence AuburnWA United States Department of Computing Sciences Nelson Mandela Metropolitan University Port Elizabeth South Africa Technical University of Ostrava Ostrava-Poruba Czech Republic Computational Intelligences and Technologies Lab Universiti Teknikal Malaysia Melaka Durian Tunggal Malaysia

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Tensor fem for spectral fractional diffusion

arXiv

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arXiv 2017年

作者： Banjai, Lehel Melenk, Jens M. Nochetto, Ricardo H. Otárola, Enrique Salgado, Abner J. Schwab, Christoph Maxwell Institute for Mathematical Sciences School of Mathematical & Computer Sciences Heriot-Watt University EdinburghEH14 4AS United Kingdom Institut für Analysis und Scientific Computing Technische Universität Wien ViennaA-1040 Austria Department of Mathematics Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Departamento de Matemática Universidad Técnica Federico Santa María Valparaíso Chile Department of Mathematics University of Tennessee KnoxvilleTN37996 United States Seminar for Applied Mathematics ETH Zürich ETH Zentrum HG G57.1 ZürichCH8092 Switzerland

We design and analyze several Finite Element Methods (FEMs) applied to the Caffarelli-Silvestre extension that localizes the fractional powers of symmetric, coercive, linear elliptic operators in bounded domains with Dirichlet boundary conditions. We consider open, bounded, polytopal but not necessarily convex domains Ω ⊂ Rd with d = 1, 2. For the solution to the extension problem, we establish analytic regularity with respect to the extended variable y ∈ (0, ∞). We prove that the solution belongs to countably normed, power–exponentially weighted Bochner spaces of analytic functions with respect to y, taking values in corner-weighted Kondat’ev type Sobolev spaces in Ω. In Ω ⊂ R2, we discretize with continuous, piecewise linear, Lagrangian FEM (P1-FEM) with mesh refinement near corners, and prove that first order convergence rate is attained for compatible data f ∈ H1−s(Ω). We also prove that tensorization of a P1-FEM in Ω with a suitable hp-FEM in the extended variable achieves log-linear complexity with respect to NΩ, the number of degrees of freedom in the domain Ω. In addition, we propose a novel, sparse tensor product FEM based on a multilevel P1FEM in Ω and on a P1-FEM on radical–geometric meshes in the extended variable. We prove that this approach also achieves log-linear complexity with respect to NΩ. Finally, under the stronger assumption that the data is analytic in Ω, and without compatibility at ∂Ω, we establish exponential rates of convergence of hp-FEM for spectral, fractional diffusion operators in energy norm. This is achieved by a combined tensor product hp-FEM for the Caffarelli-Silvestre extension in the truncated cylinder Ω × (0, Y) with anisotropic geometric meshes that are refined towards ∂Ω. We also report numerical experiments for model problems which confirm the theoretical results. We indicate several extensions and generalizations of the proposed methods to other problem classes and to other boundary conditions on ∂Ω. Copyright © 2017, The

关键词： Anisotropy

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Asynchronous In Situ Connected-Components Analysis for Complex Fluid flows

Asynchronous In Situ Connected-Components Analysis for Compl...

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Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization (ISAV)

作者： James E. McClure Mark A. Berrill Jan F. Prins Cass T. Miller Advanced Research Computing Virginia Tech Blacksburg Virginia USA Scientific Computing Group Oak Ridge National Lab Oak Ridge Tennessee USA Department of Computer Science University of North Carolina Chapel Hill North Carolina USA Department of Environmental Sciences and Engineering University of North Carolina Chapel Hill North Carolina USA

ISBN: (纸本)9781509038732

The simulation of multiscale physics is an important challenge for scientific computing. For this class of problem, large three-dimensional simulations are performed to advance scientific inquiry. On massively parallel computing systems, the volume of data generated by such approaches can become a productivity bottleneck if the raw data generated from the simulation is analyzed in a post-processing step. To address this, we present a physics-based framework for in situ data reduction that is theoretically grounded in multiscale averaging theory. We show how task parallelism can be exploited to concurrently perform a variety of analysis tasks with data-dependent costs, including the generation of iso-surfaces, morphological analyses, and connected components analysis. All analyses are performed in parallel using distributed memory and use the same domain decomposition as the simulation. A task management framework is constructed to leverage available parallelism within a node for analysis. The capabilities of the framework are to launch asynchronous analysis threads, manage dependencies between different tasks, promote data locality and minimize the impact of data transfers. The framework is applied to analyze GPU-based simulations of two-fluid-phase flow in porous media, generating a set of averaged measures that represents the overall system behavior. We demonstrate how the approach can be applied to perform physically-consistent analysis over fluid sub-regions determined from connected components analysis. Simulations performed on Oak Ridge National Lab's Titan supercomputer are profiled to demonstrate the performance of the associated multi-threaded in situ analysis approach for typical production simulation of two-fluid-phase flow.

关键词： Computational modeling Analytical models Data models Solid modeling Parallel processing Electronic mail computer architecture

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Multi-muscle TMS mapping using subject-specific FEA models of induced currents

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Brain Stimulation 2017年第4期10卷 e28-e28页

作者： Mathew Yarossi Moritz Dannhauer Deniz Erdogmus Dana Brooks Eugene Tunik Graduate School of Biomedical Sciences Rutgers Biomedical and Health Sciences Newark NJ USA Department of Biomedical Engineering New Jersey Institute of Technology Newark NJ USA Department of Rehabilitation and Movement Sciences Rutgers Biomedical Health Sciences Newark NJ USA Electrical and Computer Engineering Northeastern University Boston USA Center for Integrative Biomedical Computing University of Utah Salt Lake City USA Scientific Computing and Imaging Institute University of Utah Salt Lake City USA Department of Physical Therapy Movement and Rehabilitation Sciences Northeastern University Boston MA USA Department of Bioengineering Northeastern University Boston MA USA

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Preprocessing phase for initializing the PRSOM architecture

Preprocessing phase for initializing the PRSOM architecture

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Intelligent Systems and computer Vision (ISCV)

作者： Harchli Fidae En-Naimani Zakariae Es-Safi Abdelatif Ettaouil Mohamed Scientific Computing and Computer Sciences Engineering Sciences University of SIDI MOHAMED IBN ABDELAH FES MOROCCO

The self-organizing map (SOM) is a popular neural network which was designed for solving problems that involve tasks such as clustering and visualization. Specially, It provides a new strategy of clustering using a competition and co-operation principal. The probabilistic Kohonen network (PRSOM) is the stochastic version of classical one. However, determination of the optimal number of neurons and their initial weight vectors in the map is still a big problem in the literature. These parameters have a great impact on the learning process of the network, the convergence and the quality of results. Also determination of clusters' number of datasets is a very difficult task. In this paper we extend the original Kohonen network to classify unlabeled data and determine the number of clusters. The task consists of generating a heuristic method before the learning phase of the network. The main goal of this method is looking for the initial parameters of the map. We compare the result of the proposed method with that of the original Kohonen network. We further experiment the applicability and the performance of the method on dataset Iris. The result shows that the proposed method is able to produce satisfactory clustering results.

关键词： Neurons Clustering algorithms Iris Probabilistic logic Partitioning algorithms Neural networks Testing

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Making advanced scientific algorithms and big scientific data management more accessible 14

Making advanced scientific algorithms and big scientific dat...

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Computational Imaging XIV 2016

作者： Venkatakrishnan, S.V. Mohan, K. Aditya Beattie, Keith Correa, Joaquin Dart, Eli Deslippe, Jack R. Hexemer, Alexander Krishnan, Harinarayan MacDowell, Alastair A. Marchesini, Stefano Patton, Simon J. Perciano, Talita Sethian, James A. Stromsness, Rune Tierney, Brian L. Tull, Craig E. Ushizima, Daniela Parkinson, Dilworth Y. Center for Advanced Mathematics for Energy Research Applications Lawrence Berkeley National Lab BerkeleyCA94720 United States Advanced Light Source Lawrence Berkeley National Lab BerkeleyCA94720 United States School of Electrical and Computer Engineering Purdue University PurdueIN47907 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States National Energy Research Scientific Computing Center BerkeleyCA94720 United States Energy Sciences Network BerkeleyCA94720 United States Department of Mathematics UC Berkeley BerkeleyCA94720 United States

Synchrotrons such as the Advanced Light Source (ALS) at Lawrence Berkeley National Laboratory are user facilities - they are sources of extremely bright X-ray beams, and scientists come from all over the world to perform experiments that require these beams. As the complexity of experiments has increased, and the size and rates of data sets has exploded, managing, analyzing and presenting the data collected at synchrotrons has been an increasing challenge. The ALS has partnered with high performance computing, fast networking, and applied mathematics groups to create a"super-facility", giving users simultaneous access to the experimental, computational, and algorithmic resources to overcome this challenge. This combination forms an efficient closed loop, where data despite its high rate and volume is transferred and processed, in many cases immediately and automatically, on appropriate compute resources, and results are extracted, visualized, and presented to users or to the experimental control system, both to provide immediate insight and to guide decisions about subsequent experiments during beam-time. In this paper, We will present work done on advanced tomographic reconstruction algorithms to support users of the 3D micron-scale imaging instrument (Beamline 8.3.2, hard X-ray micro-tomography). © 2016 Society for Imaging Science and Technology.

关键词： Tomography

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A fully implicit method for lattice boltzmann equations

A fully implicit method for lattice boltzmann equations

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作者： Huang, Jizu Yang, Chao Cai, Xiao-Chuan Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Institute of Software Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Computer Science Beijing100190 China Department of Computer Science University of Colorado Boulder BoulderCO80309 United States

Existing approaches for solving the lattice Boltzmann equations with finite difference methods are explicit and semi-implicit;both have certain stability constraints on the time step size. In this work, a fully implicit second-order finite difference scheme is developed. We focus on a parallel, highly scalable, Newton-Krylov-RAS algorithm for the solution of a large sparse nonlinear system of equations arising at each time step. Here, RAS is a restricted additive Schwarz preconditioner based on a first-order spatial discretization. We show numerically that by using the fully implicit method the time step size is no longer constrained by the CFL condition, and the Newton-Krylov- RAS algorithm is scalable on a supercomputer with more than ten thousand processors. Moreover, to calculate the steady state solution we investigate an adaptive time stepping strategy. The total compute time required by the implicit method with adaptive time stepping is much smaller than that of an explicit method for several test cases. © 2015 Society for Industrial and Applied Mathematics.

关键词： Finite difference method

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Outcomes from the DOE workshop on turbulent flow simulation at the exascale 46

Outcomes from the DOE workshop on turbulent flow simulation ...

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46th AIAA Fluid Dynamics Conference, 2016

作者： Sprague, Michael A. Boldyrev, Stanislav Chang, Choong-Seock Fischer, Paul Grout, Ray Gustafson, William I. Hittinger, Jeffrey A. F. Merzari, Elia Moser, Robert National Renewable Energy Laboratory 15013 DenverWest Parkway GoldenCO80401 United States University of Wisconsin-Madison MadisonWI53706 United States Princeton University PrincetonNJ08543 United States University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Renewable Energy Laboratory GoldenCO80401 United States Pacific Northwest National Laboratory RichlandWA99352 United States Lawrence Livermore National Laboratory LivermoreCA94551 United States Argonne National Laboratory ArgonneIL60439 United States University of Texas at Austin AustinTX78712 United States Computational Science Center 15013 DenverWest Parkway United States Physics Department 1150 University Avenue United States Princeton Plasma Physics Laboratory P.O. Box 451 United States Department of Computer Science 201 North Goodwin Avenue United States Computational Science Center 5013 Denver West Parkway United States Atmospheric Sciences and Global Change Division P.O. Box 999 K9-30 United States Scientific Computing Group Center for Applied Scientific Computing PO Box 808 L-561 United States Mathematics and Computer Science 9700 S. Cass Avenue United States Institute for Computational Engineering and Sciences and Department of Mechanical Engineering 201 East 24th United States

ISBN: (数字)9781624104367

ISBN: (纸本)9781624104367

This paper summarizes the outcomes from the Turbulent Flow Simulation at the Exascale: Opportunities and Challenges Workshop, which was held 4-5 August 2015, and was sponsored by the U.S. Department of Energy (DOE) Office of Advanced scientific computing Research (ASCR). The workshop objectives were to define and describe the challenges and opportunities that computing at the exascale will bring to turbulent-flow simulations in applied science and technology. The need for accurate simulation of turbulent flows is evident across the DOE applied-science and engineering portfolios, including combustion, plasma physics, nuclear-reactor physics, wind energy, and atmospheric science. The work-shop brought together experts in turbulent-flow simulation, computational mathematics, and high-performance computing. Building upon previous ASCR workshops on exascale computing, participants defined a research agenda and path forward that will enable scientists and engineers to continually leverage, engage, and direct advances in computational systems on the path to exascale computing. © 2016, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Turbulent flow

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