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Multifunctional Composites: Optimizing Microstructures for Simultaneous Transport of Heat and Electricity

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Physical Review Letters 2002年第26期89卷 266601-266601页

作者： S. Torquato S. Hyun A. Donev Princeton Materials Institute Princeton University Princeton New Jersey 08544 Department of Chemistry Princeton University Princeton New Jersey 08544 Program in Applied & Computational Mathematics Princeton University Princeton New Jersey 08544

Composite materials are ideally suited to achieve multifunctionality since the best features of different materials can be combined to form a new material that has a broad spectrum of desired properties. Nature’s ultimate multifunctional composites are biological materials. There are presently no simple examples that rigorously demonstrate the effect of competing property demands on composite microstructures. To illustrate the fascinating types of microstructures that can arise in multifunctional optimization, we maximize the simultaneous transport of heat and electricity in three-dimensional, two-phase composites using rigorous optimization techniques. Interestingly, we discover that the optimal three-dimensional structures are bicontinuous triply periodic minimal surfaces.

关键词： .arise bicontinuous periodic minimal triply Simultaneous Transport Heat and Electricity multifunctional optimization

English-Chinese Translation using Neural Machine Models

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English-Chinese Translation using Neural Machine Models

Telecommunications, Optics and Computer Science (TOCS), IEEE Conference on

作者： Qi Liu Pengfei Ou Applied and Computational Mathematical Sciences Program University of Washington Seattle the United States Department of Mathematics University of Illinois at Urbana Champaign Urbana the United States

ISBN: (纸本)9781665470544

During the last few years, neural machine translation (NMT) as a notable branch of machine translation has been increasing its popularity both in research and in practice. In particular, neural machine translation between English and Chinese, which are two of the most widely used languages all over the world, is receiving more and more attention. In this review, we discuss current mainstream models for neural machine translation, including recurrent neural network (RNN), convolution neural network (CNN), and self-attention network (SAN) or transformer. The mechanisms of these models are illustrated. Moreover, examples of studies and applications of them are analyzed. In addition, comparisons on the performance of different models are implemented.

关键词： Recurrent neural networks Training data Transformers Optics Tokenization Data models Telecommunications

Modeling subgrid-scale forces by spatial artificial neural networks in large eddy simulation of turbulence

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Physical Review Fluids 2020年第5期5卷 054606-054606页

作者： Chenyue Xie Jianchun Wang Weinan E Shenzhen Key Laboratory of Complex Aerospace Flows Center for Complex Flows and Soft Matter Research Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 People's Republic of China Department of Mathematics Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

Spatial artificial neural network (ANN) models are developed for subgrid-scale (SGS) forces in the large eddy simulation (LES) of turbulence. The input features are based on the first-order derivatives of the filtered velocity field at different spatial locations. The correlation coefficients of SGS forces predicted by the spatial artifical neural network (SANN) models with reasonable spatial stencil geometry can be made larger than 0.99 in an a priori analysis, and the relative error of SGS forces can be made smaller than 15%, much smaller than that of the traditional gradient model. In a posteriori analysis, a detailed comparison is made on the results of LES using the SANN model, implicit large eddy simulation (ILES), the dynamic Smagorinsky model (DSM), and the dynamic mixed model (DMM) at grid resolution of 643. It is shown that the SANN model performs better than the ILES, DSM, and DMM models in the prediction of the spectrum and other statistical properties of the velocity field, as well as the instantaneous flow structures. These results suggest that artificial neural network with consideration of spatial characteristics is a very effective tool for developing advanced SGS models in LES of turbulence.

关键词： Compressible flows Turbulence Artificial neural networks

Communication-computation efficient gradient coding

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arXiv 2018年

作者： Ye, Min Abbe, Emmanuel Department of Electrical Engineering Princeton University PrincetonNJ United States Program in Applied and Computational Mathematics Department of Electrical Engineering Princeton University School of Mathematics Institute for Advanced Study PrincetonNJ08544 United States

This paper develops coding techniques to reduce the running time of distributed learning tasks. It characterizes the fundamental tradeoff to compute gradients (and more generally vector summations) in terms of three parameters: computation load, straggler tolerance and communication cost. It further gives an explicit coding scheme that achieves the optimal tradeoff based on recursive polynomial constructions, coding both across data subsets and vector components. As a result, the proposed scheme allows to minimize the running time for gradient computations. Implementations are made on Amazon EC2 clusters using Python with mpi4py package. Results show that the proposed scheme maintains the same generalization error while reducing the running time by 32% compared to uncoded schemes and 23% compared to prior coded schemes focusing only on stragglers (Tandon et al., ICML 2017). Copyright © 2018, The Authors. All rights reserved.

关键词： Python

Plane-wave analysis of a hyperbolic system of equations with relaxation in Rd

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arXiv 2018年

作者： de Hoop, Maarten V. Liu, Jian-Guo Markowich, Peter A. Ussembayev, Nail S. Department of Computational and Applied Mathematics Rice University HoustonTX77005 United States Department of Mathematics Department of Physics Duke University DurhamNC27708 United States Applied Mathematics and Computer Science Program CEMSE Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Faculty of Mathematics University of Vienna ViennaA-1090 Austria Applied Mathematics and Computer Science Program CEMSE Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We consider a multi-dimensional scalar wave equation with memory corresponding to the viscoelastic material described by a generalized Zener model. We deduce that this relaxation system is an example of a non-strictly hyperbolic system satisfying Majda's block structure condition. Well-posedness of the associated Cauchy problem is established by showing that the symbol of the spatial derivatives is uniformly diagonalizable with real eigenvalues. A long-time stability result is obtained by plane-wave analysis when the memory term allows for dissipation of energy.35L40 (Primary) 74D05, 35B35 (Secondary) Copyright © 2018, The Authors. All rights reserved.

关键词： Viscoelasticity

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

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arXiv 2017年

作者： Wang, Han Zhang, Linfeng Han, Jiequn Weinan, E. Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China CAEP Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Center for Data Science Beijing International Center for Mathematical Research Peking University Beijing Institute of Big Data Research Beijing100871 China

Recent developments in many-body potential energy representation via deep learning have brought new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Here we describe DeePMD-kit, a package written in Python/C++ that has been designed to minimize the effort required to build deep learning based representation of potential energy and force field and to perform molecular dynamics. Potential applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems. DeePMD-kit is interfaced with TensorFlow, one of the most popular deep learning frameworks, making the training process highly automatic and efficient. On the other end, DeePMD-kit is interfaced with high-performance classical molecular dynamics and quantum (path-integral) molecular dynamics packages, i.e., LAMMPS and the i-PI, respectively. Thus, upon training, the potential energy and force field models can be used to perform efficient molecular simulations for different purposes. As an example of the many potential applications of the package, we use DeePMD-kit to learn the interatomic potential energy and forces of a water model using data obtained from density functional theory. We demonstrate that the resulted molecular dynamics model reproduces accurately the structural information contained in the original model. Copyright © 2017, The Authors. All rights reserved.

关键词： Potential energy

Reed-Muller codes polarize

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arXiv 2019年

作者： Abbe, Emmanuel Ye, Min Mathematics Institute School of Computer and Communication Sciences Epfl Switzerland Program in Applied and Computational Mathematics Department of Electrical Engineering Princeton University United States Department of Electrical Engineering Princeton University PrincetonNJ United States

Reed-Muller (RM) codes and polar codes are generated by the same matrix Gm= [1 0/1 1]⊗mbut using different subset of rows. RM codes select simply rows having largest weights. Polar codes select instead rows having the largest conditional mutual information proceeding top to down in Gm;while this is a more elaborate and channel-dependent rule, the top-to-down ordering has the advantage of making the conditional mutual information polarize, giving directly a capacity-achieving code on any binary memoryless symmetric channel (BMSC). RM codes are yet to be proved to have such property. In this paper, we reconnect RM codes to polarization theory. It is shown that proceeding in the RM code ordering, i.e., not top-to-down but from the lightest to the heaviest rows in Gm, the conditional mutual information again polarizes. We further demonstrate that it does so faster than for polar codes. This implies that Gmcontains another code, different than the polar code and called here the twin code, that is provably capacity-achieving on any BMSC. This proves a necessary condition for RM codes to achieve capacity on BMSCs. It further gives a sufficient condition if the rows with largest conditional mutual information correspond to the heaviest rows, i.e., if the twin code is the RM code. We show here that the two codes bare similarity with each other and give further evidence that they are likely the same. Copyright © 2019, The Authors. All rights reserved.

关键词： Forward error correction

Analytic form for a nonlocal kinetic energy functional with a density-dependent kernel for orbital-free density functional theory under periodic and Dirichlet boundary conditions

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Physical Review B 2008年第4期78卷 045105-045105页

作者： Gregory S. Ho Vincent L. Lignères Emily A. Carter Department of Chemistry Princeton University Princeton New Jersey 08544 USA Department of Mechanical and Aerospace Engineering and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544-5263 USA

We derive an analytic form of the Wang-Govind-Carter (WGC) [Wang et al., Phys. Rev. B 60, 16350 (1999)] kinetic energy density functional (KEDF) with the density-dependent response kernel. A real-space aperiodic implementation of the WGC KEDF is then described and used in linear scaling orbital-free density functional theory (OF-DFT) calculations.

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Extracting Kinetic and Stationary Distribution Information from Short MD Trajectories via a Collection of Surrogate Diffusion Models. (vol 5, pg 47, 2009)

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JOURNAL OF CHEMICAL THEORY AND COMPUTATION 2009年第8期5卷 2192-2192页

作者： Calderon, Christopher P. Arora, Karunesh Department of Computational and Applied Mathematics and Department of Statistics Rice University Houston Texas 77005-1892 and Department of Chemistry Biophysics Program University of Michigan 930 North University Avenue Ann Arbor Michigan 48109

Low-dimensional stochastic models can summarize dynamical information and make long time predictions associated with observables of complex atomistic systems. Maximum likelihood based techniques for estimating low-dimensional surrogate diffusion models from relatively short time series are presented. It is found that a heterogeneous population of slowly evolving conformational degrees of freedom modulates the dynamics. This underlying heterogeneity results in a collection of estimated low-dimensional diffusion models. Numerical techniques for exploiting this finding to approximate skewed histograms associated with the simulation are presented. In addition, statistical tests are used to assess the validity of the models and determine physically relevant sampling information, e.g. the maximum sampling frequency at which one can discretely sample from an atomistic time series and have a surrogate diffusion model pass goodness-of-fit tests. The information extracted from such analyses can possibly be used to assist umbrella sampling computations as well as help in approximating effective diffusion coefficients. The techniques are demonstrated on simulations of adenylate kinase.

关键词： Transport properties Diffusion Approximation computational chemistry Computer simulations