arXiv: Computational Physics最新文献_第5页

Smooth-Wall Boundary Conditions for Energy-Dissipation Turbulence Models# 能量耗散湍流模型的光滑壁边界条件

arXiv: Computational Physics Pub Date : 2019-07-30 DOI: 10.35840/2631-5009/7525

D. Hunsaker, W. Phillips, R. Spall

引用次数: 0

An iterative scheme for the generalized Peierls–Nabarro model based on the inverse Hilbert transform 基于逆希尔伯特变换的广义Peierls-Nabarro模型的迭代格式

arXiv: Computational Physics Pub Date : 2019-07-02 DOI: 10.1142/S2047684119500192

A. A. Ramabathiran

引用次数: 0

Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery 开发一种改进的晶体图卷积神经网络框架，用于加速材料发现

arXiv: Computational Physics Pub Date : 2019-06-12 DOI: 10.1103/PHYSREVMATERIALS.4.063801

Cheol Woo Park, C. Wolverton

{"title":"Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery","authors":"Cheol Woo Park, C. Wolverton","doi":"10.1103/PHYSREVMATERIALS.4.063801","DOIUrl":"https://doi.org/10.1103/PHYSREVMATERIALS.4.063801","url":null,"abstract":"The recently proposed crystal graph convolutional neural network (CGCNN) offers a highly versatile and accurate machine learning (ML) framework by learning material properties directly from graph-like representations of crystal structures (\"crystal graphs\"). Here, we develop an improved variant of the CGCNN model (iCGCNN) that outperforms the original by incorporating information of the Voronoi tessellated crystal structure, explicit 3-body correlations of neighboring constituent atoms, and an optimized chemical representation of interatomic bonds in the crystal graphs. We demonstrate the accuracy of the improved framework in two distinct illustrations: First, when trained/validated on 180,000/20,000 density functional theory (DFT) calculated thermodynamic stability entries taken from the Open Quantum Materials Database (OQMD) and evaluated on a separate test set of 230,000 entries, iCGCNN achieves a predictive accuracy that is significantly improved, i.e., 20% higher than that of the original CGCNN. Second, when used to assist high-throughput search for materials in the ThCr2Si2 structure-type, iCGCNN exhibited a success rate of 31% which is 310 times higher than an undirected high-throughput search and 2.4 times higher than that of the original CGCNN. Using both CGCNN and iCGCNN, we screened 132,600 compounds with elemental decorations of the ThCr2Si2 prototype crystal structure and identified a total of 97 new unique stable compounds by performing 757 DFT calculations, accelerating the computational time of the high-throughput search by a factor of 130. Our results suggest that the iCGCNN can be used to accelerate high-throughput discoveries of new materials by quickly and accurately identifying crystalline compounds with properties of interest.","PeriodicalId":8424,"journal":{"name":"arXiv: Computational Physics","volume":"46 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90490760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 141

WITHDRAWN: Modeling tissue perfusion in terms of 1d-3d embedded mixed-dimension coupled problems with distributed sources 撤回:用分布源的1d-3d嵌入式混合维耦合问题建模组织灌注

arXiv: Computational Physics Pub Date : 2019-05-02 DOI: 10.1016/j.jcpx.2020.100050

T. Koch, M. Schneider, R. Helmig, P. Jenny

引用次数: 6

Atomic-orbital-free intrinsic ferromagnetism in electrenes 电子中无原子轨道的本征铁磁性

arXiv: Computational Physics Pub Date : 2019-04-10 DOI: 10.1103/PHYSREVB.102.180407

Jun Zhou, Y. Feng, Lei Shen

引用次数: 10

Verification of Two-Dimensional Monte Carlo Ray-Trace Methodology in Radiation Heat Transfer Analysis 二维蒙特卡罗射线追踪方法在辐射传热分析中的验证

arXiv: Computational Physics Pub Date : 2018-10-11 DOI: 10.13140/RG.2.2.12632.14089

M. Yarahmadi, J. Mahan

引用次数: 2

HEP Software Foundation Community White Paper Working Group - Data and Software Preservation to Enable Reuse HEP软件基金会社区白皮书工作组-数据和软件保存以实现重用

arXiv: Computational Physics Pub Date : 2018-10-02 DOI: 10.2172/1488605

M. Hildreth, A. Boehnlein, K. Cranmer, S. Dallmeier, R. Gardner, T. Hacker, L. Heinrich, I. Jimenez, M. Kane, D. Katz, T. Malik, C. Maltzahn, M. Neubauer, S. Neubert, J. Pivarski, E. Sexton, J. Shiers, T. Simko, S. Smith, D. South, A. Verbytskyi, G. Watts, J. Woźniak

引用次数: 1

Comparison of Numerical Approaches to the Time-Dependent Schr"odinger Solutions in One Dimension. 一维时变Schr odinger解的数值方法比较。

arXiv: Computational Physics Pub Date : 2018-09-24 DOI: 10.1002/HTTPS://DOI.ORG/10.1016/J.CPC.2019.05.019

H. Gharibnejad, B. Schneider, M. Leadingham, H. J. Schmale

引用次数: 0

Thermal Convection in the van der Waals Fluid 范德华流体中的热对流

arXiv: Computational Physics Pub Date : 2018-09-04 DOI: 10.1007/978-3-319-96469-0_9

Ju Liu

引用次数: 3

Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence. 辐照粒子负载湍流不确定度量化及灵敏度分析的双保真近似。

arXiv: Computational Physics Pub Date : 2018-08-01 DOI: 10.2172/1463950

Hillary R. Fairbanks, L. Jofre, G. Geraci, G. Iaccarino, A. Doostan

{"title":"Bi-fidelity approximation for uncertainty quantification and sensitivity analysis of irradiated particle-laden turbulence.","authors":"Hillary R. Fairbanks, L. Jofre, G. Geraci, G. Iaccarino, A. Doostan","doi":"10.2172/1463950","DOIUrl":"https://doi.org/10.2172/1463950","url":null,"abstract":"Efficiently performing predictive studies of irradiated particle-laden turbulent flows has the potential of providing significant contributions towards better understanding and optimizing, for example, concentrated solar power systems. As there are many uncertainties inherent in such flows, uncertainty quantification is fundamental to improve the predictive capabilities of the numerical simulations. For large-scale, multi-physics problems exhibiting high-dimensional uncertainty, characterizing the stochastic solution presents a significant computational challenge as many methods require a large number of high-fidelity solves. This requirement results in the need for a possibly infeasible number of simulations when a typical converged high-fidelity simulation requires intensive computational resources. To reduce the cost of quantifying high-dimensional uncertainties, we investigate the application of a non-intrusive, bi-fidelity approximation to estimate statistics of quantities of interest associated with an irradiated particle-laden turbulent flow. This method relies on exploiting the low-rank structure of the solution to accelerate the stochastic sampling and approximation processes by means of cheaper-to-run, lower fidelity representations. The application of this bi-fidelity approximation results in accurate estimates of the QoI statistics while requiring a small number of high-fidelity model evaluations.","PeriodicalId":8424,"journal":{"name":"arXiv: Computational Physics","volume":"439 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82869302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13