Solving Inverse Wave Scattering with Deep LearningThis paper proposes a neural network approach for solving two classical problems in the two-dimensional inverse wave scattering: far field pattern problem and seismic imaging. The mathematical…Yuwei Fan, Lexing Ying·Nov 27, 2019SaveLearn
Overcoming the Convergence Difficulty of Cohesive Zone Models through a Newton-Raphson Modification TechniqueThis paper studies the convergence difficulty of cohesive zone models in static analysis. It is shown that an inappropriate starting point of iterations in the Newton-Raphson method is responsible…Reza Sepasdar, Maryam Shakiba·Nov 27, 2019SaveLearn
First principles calculations on theoretical band gap improvement of IIIA-VA zinc-blende semiconductor InAsThe structural, electronic, dielectric and vibrational properties of zinc-blende (ZB) InAs were studied within the framework of density functional theory (DFT) by employing local density…Waqas Mahmood, Arfan Bukhtiar, Muhammad Haroon et al.·Nov 27, 2019SaveLearn
Unbiasing the initiator approximation in Full Configuration Interaction Quantum Monte CarloWe identify and rectify a crucial source of bias in the initiator FCIQMC algorithm. Non-initiator determinants (i.e. determinants whose population is below the initiator threshold) are subject to a…Khaldoon Ghanem, Alexander Y. Lozovoi, Ali Alavi·Nov 26, 2019SaveLearn
Physics enhanced neural networks predict order and chaosConventional artificial neural networks are powerful tools in science and industry, but they can fail when applied to nonlinear systems where order and chaos coexist. We use neural networks that…Anshul Choudhary, John F. Lindner, Elliott G. Holliday et al.·Nov 26, 2019SaveLearn
Effects of different discretisations of the Laplacian upon stochastic simulations of reaction-diffusion systems on both static and growing domainsBy discretising space into compartments and letting system dynamics be governed by the reaction-diffusion master equation, it is possible to derive and simulate a stochastic model of reaction and…Bartosz J. Bartmanski, Ruth E. Baker·Nov 26, 2019SaveLearn
Imaging Mechanism for Hyperspectral Scanning Probe Microscopy via Gaussian Process ModellingWe investigate the ability to reconstruct and derive spatial structure from sparsely sampled 3D piezoresponse force microcopy data, captured using the band-excitation (BE) technique, via Gaussian…Maxim Ziatdinov, Dohyung Kim, Sabine Neumayer et al.·Nov 26, 2019SaveLearn
Modified HLLC-VOF solver for incompressible two-phase fluid flowsA modified HLLC-type contact preserving Riemann solver for incompressible two-phase flows using the artificial compressibility formulation is presented. Here, the density is omitted from the pressure…Sourabh P. Bhat, J. C. Mandal·Nov 25, 2019SaveLearn
Relativistic density functional theory with finite-light-speed correction for the Coulomb interaction: a non-relativistic-reduction based approachThe Breit correction, the finite-light-speed correction for the Coulomb interaction of the electron-electron interaction in O ( 1/ c2 ) , is introduced to density functional theory…Tomoya Naito, Ryosuke Akashi, Haozhao Liang et al.·Nov 25, 2019SaveLearn
Application of k . p method on band structure of GaAs obtained through joint density functional theoryThe structural and electronic properties of zinc-blende (ZB) GaAs were calculated within the framework of plane wave density-functional theory (DFT) code JDFTx by using Becke 86 in 2D and PBE…Waqas Mahmood, Bing Dong·Nov 25, 2019SaveLearn
Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory Based Ab Initio Molecular Dynamics I: Theory, Algorithm, and PerformanceBy including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semi-local density functional theory (DFT), and thereby furnish a more accurate and reliable…Hsin-Yu Ko, Junteng Jia, Biswajit Santra et al.·Nov 24, 2019SaveLearn
An Arc-Length Approximation For Elliptical OrbitsIn this paper, we overlay a continuum of analytical relations which essentially serve to compute the arc-length described by a celestial body in an elliptic orbit within a stipulated time interval.…Aayush Jha, Ashim B. Karki·Nov 24, 2019SaveLearn
Modified deformation behaviour of self-ion irradiated tungsten: A combined nano-indentation, HR-EBSD and crystal plasticity studyPredicting the dramatic changes in material properties caused by irradiation damage is key for the design of future nuclear fission and fusion reactors. Self-ion implantation is an attractive tool…Suchandrima Das, Hongbing Yu, Kenichiro Mizohata et al.·Nov 23, 2019SaveLearn
Many-body calculations for periodic materials via quantum machine learningA state-of-the-art method that combines a quantum computational algorithm and machine learning, so-called quantum machine learning, can be a powerful approach for solving quantum many-body problems.…Shu Kanno, Tomofumi Tada·Nov 23, 2019SaveLearn
Modeling the vertical growth of van der Waals stacked 2D materials using the diffuse domain methodVertically-stacked monolayers of graphene and other atomically-thin 2D materials have attracted considerable research interest because of their potential in fabricating materials with…Zhenlin Guo, Christopher Price, Vivek B. Shenoy et al.·Nov 22, 2019SaveLearn
A fast multi-resolution lattice Green's function method for elliptic difference equationsWe propose a mesh refinement technique for solving elliptic difference equations on unbounded domains based on the fast lattice Green's function (FLGF) method. The FLGF method exploits the…Benedikt Dorschner, Ke Yu, Gianmarco Mengaldo et al.·Nov 22, 2019SaveLearn
Challenges in fluid flow simulations using Exascale computingIn this paper, I discuss the challenges in porting hydrodynamic codes to futuristic exascale HPC systems. In particular, we describe the computational complexities of finite difference method,…Mahendra K. Verma·Nov 22, 2019SaveLearn
On the closure requirement for VOF simulations with RANS modelingThe volume of fluid (VOF) method is increasingly used in computational fluid dynamics (CFD) simulations of turbulent two-phase flows. The Reynolds-Averaged Navier-Stokes (RANS) approach is an…Wenyuan Fan, Henryk Anglart·Nov 21, 2019SaveLearn
Automatic Differentiable Monte Carlo: Theory and ApplicationDifferentiable programming has emerged as a key programming paradigm empowering rapid developments of deep learning while its applications to important computational methods such as Monte Carlo…Shi-Xin Zhang, Zhou-Quan Wan, Hong Yao·Nov 20, 2019SaveLearn
Finite Temperature Phase Behavior of Viral Capsids as Oriented Particle ShellsA general phase-plot is proposed for discrete particle shells that allows for thermal fluctuations of the shell geometry and of the inter-particle connectivities. The phase plot contains a…Amit Rajnarayan Singh, Andrej Košmrlj, Robijn F. Bruinsma·Nov 20, 2019SaveLearn
Impressive computational acceleration by using machine learning for 2-dimensional super-lubricant materials discoveryThe screening of novel materials is an important topic in the field of materials science. Although traditional computational modeling, especially first-principles approaches, is a very useful and…Marco Fronzi, Mutaz Abu Ghazaleh, Olexandr Isayev et al.·Nov 20, 2019SaveLearn
A greedy algorithm for computing eigenvalues of a symmetric matrixWe present a greedy algorithm for computing selected eigenpairs of a large sparse matrix H that can exploit localization features of the eigenvector. When the eigenvector to be computed is…Taylor M. Hernandez, Roel Van Beeumen, Mark A. Caprio et al.·Nov 20, 2019SaveLearn
Towards Physics-informed Deep Learning for Turbulent Flow PredictionWhile deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and…Rui Wang, Karthik Kashinath, Mustafa Mustafa et al.·Nov 20, 2019SaveLearn
Coupled MHD -- Hybrid Simulations of Space PlasmasHeliospheric plasmas require multi-scale and multi-physics considerations. On one hand, MHD codes are widely used for global simulations of the solar-terrestrial environments, but do not provide the…S. P. Moschou, I. V. Sokolov, O. Cohen et al.·Nov 20, 2019SaveLearn
Stochastic estimations of a total number of classes for the clusterings with too enormous samples to be accommodate into a clustering engineWe considered the problem how to handle the exploding number of possibilities to be sorted into irreducible classes by using a clustering tool when its input capacity cannot accommodate the total…Keishu Utimula, Genki I. Prayogo, Kousuke Nakano et al.·Nov 19, 2019SaveLearn