Simflowny 3: An upgraded platform for scientific modelling and simulationSimflowny is an open platform which automatically generates efficient parallel code of scientific dynamical models for different simulation frameworks. Here we present major upgrades on this software…C. Palenzuela, B. Miñano, A. Arbona et al.·Oct 2, 2020SaveLearn
Optimized computation of tight focusing of short pulses using mapping to periodic spaceWhen a pulsed, few-cycle electromagnetic wave is focused by optics with f-number smaller than two, the frequency components it contains are focused to different regions of space, building up a…Elena Panova, Valentin Volokitin, Evgeny Efimenko et al.·Oct 1, 2020SaveLearn
A machine learning framework for LES closure termsIn the present work, we explore the capability of artificial neural networks (ANN) to predict the closure terms for large eddy simulations (LES) solely from coarse-scale data. To this end, we derive…Marius Kurz, Andrea Beck·Oct 1, 2020SaveLearn
Data-driven cardiovascular flow modeling: examples and opportunitiesHigh-fidelity modeling of blood flow is crucial for enhancing our understanding of cardiovascular disease. Despite significant advances in computational and experimental characterization of blood…Amirhossein Arzani, Scott T. M. Dawson·Sep 30, 2020SaveLearn
Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics SimulationsSimulation of turbulent flows at high Reynolds number is a computationally challenging task relevant to a large number of engineering and scientific applications in diverse fields such as climate…Jaideep Pathak, Mustafa Mustafa, Karthik Kashinath et al.·Sep 30, 2020SaveLearn
A Supervised Machine Learning Approach for Accelerating the Design of Particulate Composites: Application to Thermal ConductivityA supervised machine learning (ML) based computational methodology for the design of particulate multifunctional composite materials with desired thermal conductivity (TC) is presented. The design…Mohammad Saber Hashemi, Masoud Safdari, Azadeh Sheidaei·Sep 30, 2020SaveLearn
Thermal conductivity of B-DNAThe thermal conductivity of B-form double-stranded DNA (dsDNA) of the Drew-Dickerson sequence d(CGCGAATTCGCG) is computed using classical Molecular Dynamics (MD) simulations. In contrast to previous…Vignesh Mahalingam, Dineshkumar Harursampath·Sep 30, 2020SaveLearn
A multi-scale DNN algorithm for nonlinear elliptic equations with multiple scalesAlgorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community. DNN based algorithms are easy to implement, natural for nonlinear…Xi-An Li, Zhi-Qin John Xu, Lei Zhang·Sep 30, 2020SaveLearn
Some improvements on Moment-of-Fluid method in 3D rectangular hexahedronsThe moment-of-fluid method (MOF) is an extension of the volume-of-fluid method with piecewise linear interface construction (VOF-PLIC). In MOF reconstruction, the optimized normal vector is…Zhouteng Ye, Mark Sussman, Xizeng Zhao·Sep 30, 2020SaveLearn
Fast solution of the superconducting dynamo benchmark problemA model of high temperature superconducting dynamo, a promising type of flux pumps capable of wireless injection of a large DC current into a superconducting circuit, has recently been chosen as an…Leonid Prigozhin, Vladimir Sokolovsky·Sep 30, 2020SaveLearn
Classical Density Functional Theory applied to the solid stateThe standard model of classical Density Functional Theory for pair potentials consists of a hard-sphere functional plus a mean-field term accounting for long ranged attraction. However, most…James F. Lutsko, Cédric Schoonen·Sep 30, 2020SaveLearn
A Diffuse Interface Model of Reactive-fluids and Solid-dynamicsThis article presents a multi-physics methodology for the numerical simulation of physical systems that involve the non-linear interaction of multi-phase reactive fluids and elastoplastic solids,…Tim Wallis, Philip T. Barton, Nikolaos Nikiforakis·Sep 30, 2020SaveLearn
EZFF: Python Library for Multi-Objective Parameterization and Uncertainty Quantification of Interatomic Forcefields for Molecular DynamicsParameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of…Aravind Krishnamoorthy, Ankit Mishra, Deepak Kamal et al.·Sep 30, 2020SaveLearn
TensorBNN: Bayesian Inference for Neural Networks using TensorflowTensorBNN is a new package based on TensorFlow that implements Bayesian inference for modern neural network models. The posterior density of neural network model parameters is represented as a point…Braden Kronheim, Michelle Kuchera, Harrison Prosper·Sep 30, 2020SaveLearn
Free Energy Perturbation Theory at Low TemperatureThe perturbative expansion introduced by Zwanzig [R. W. Zwanzig, J. Chem. Phys. 22, 1420 (1954)] expresses the difference in Helmholtz free energy between a system of interest and that of a…C. W. Greeff·Sep 29, 2020SaveLearn
Graph Theory Based Approach to Characterize Self Interstitial Defect MorphologyThe defect morphology is an essential aspect of the evolution of crystals' microstructure and its response to stress. Existing methods either only report defect concentration or characterize only…Utkarsh Bhardwaj, Andrea E. Sand, Manoj Warrier·Sep 29, 2020SaveLearn
Space-charge effects in the field-assisted thermionic emission from nonuniform cathodesWe use computational simulations to study the electron emission and propagation in planar vacuum diodes. We show how space-charge affects thermionic emission from cathodes with two different values…Anna Sitek, Kristinn Torfason, Andrei Manolescu et al.·Sep 28, 2020SaveLearn
Some Recent Developments in Auxiliary-Field Quantum Monte Carlo for Real MaterialsThe auxiliary-field quantum Monte Carlo (AFQMC) method is a general numerical method for correlated many-electron systems, which is being increasingly applied in lattice models, atoms, molecules, and…Hao Shi, Shiwei Zhang·Sep 28, 2020SaveLearn
Uncertainty Quantification in Atomistic Modeling of Metals and its Effect on Mesoscale and Continuum Modeling A ReviewThe design of next-generation alloys through the Integrated Computational Materials Engineering (ICME) approach relies on multi-scale computer simulations to provide thermodynamic properties when…Joshua J. Gabriel, Noah H. Paulson, Thien C. Duong et al.·Sep 28, 2020SaveLearn
Towards the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant spaceWhile sophisticated numerical methods for studying equilibrium states have well advanced, quantitative predictions of kinetic behaviour remain challenging. We introduce a reactant-to-barrier (R2B)…Stefan Heinen, Guido Falk von Rudorff, O. Anatole von Lilienfeld·Sep 28, 2020SaveLearn
Compressible lattice Boltzmann methods with adaptive velocity stencils: An interpolation-free formulationAdaptive lattice Boltzmann methods (LBMs) are based on velocity discretizations that self-adjust to local macroscopic conditions such as velocity and temperature. While this feature improves the…C. Coreixas, J. Latt·Sep 28, 2020SaveLearn
Learning Thermodynamically Stable and Galilean Invariant Partial Differential Equations for Non-equilibrium FlowsIn this work, we develop a method for learning interpretable, thermodynamically stable and Galilean invariant partial differential equations (PDEs) based on the Conservation-dissipation Formalism of…Juntao Huang, Zhiting Ma, Yizhou Zhou et al.·Sep 28, 2020SaveLearn
Sparse Gaussian Process Potentials: Application to Lithium Diffusivity in Superionic Conducting Solid ElectrolytesFor machine learning of interatomic potentials a scalable sparse Gaussian process regression formalism is introduced with a data-efficient on-the-fly adaptive sampling algorithm. With this approach,…Amir Hajibabaei, Chang Woo Myung, Kwang S. Kim·Sep 28, 2020SaveLearn
Extraction of Material Properties through Multi-fidelity Deep Learning from Molecular Dynamics SimulationSimulation of reasonable timescales for any long physical process using molecular dynamics (MD) is a major challenge in computational physics. In this study, we have implemented an approach based on…Mahmudul Islam, Md Shajedul Hoque Thakur, Satyajit Mojumder et al.·Sep 28, 2020SaveLearn
DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networksElectroconvection is a multiphysics problem involving coupling of the flow field with the electric field as well as the cation and anion concentration fields. For small Debye lengths, very steep…Shengze Cai, Zhicheng Wang, Lu Lu et al.·Sep 27, 2020SaveLearn