Multiphase Flow Modelling in Multiscale Porous Media: An Open-Sourced Micro-Continuum ApproachAn open-sourced multiphase Darcy-Brinkman approach is proposed to simulate two-phase flow in hybrid systems containing both solid-free regions and porous matrices. This micro-continuum model is…Francisco J. Carrillo, Ian C. Bourg, Cyprien Soulaine·Mar 18, 2020SaveLearn
Testing and validating AnTraGoS algorithms with impact beating spattersThe reconstruction of the area of origin of spatter patterns is usually a fundamental step to the determination of the area of the crime scene where the victim was wounded. In this field, for almost…Francesco Camana, Massimiliano Gori, Luca De Rosa et al.·Mar 18, 2020SaveLearn
Automated calculation and convergence of defect transport tensorsDefect transport is a key process in materials science and catalysis, but as migration mechanisms are often too complex to enumerate a priori, calculation of transport tensors typically have no…Thomas D Swinburne, Danny Perez·Mar 17, 2020SaveLearn
Force approach for the pseudopotential lattice Boltzmann methodThe pseudopotential method is one of the most popular extensions of the lattice Boltzmann method (LBM) for phase change and multiphase flow simulation. One attractive feature of the original proposed…L. E. Czelusniak, V. P. Mapelli, M. S. Guzella et al.·Mar 17, 2020SaveLearn
Deep learning for thermal plasma simulation: solving 1-D arc model as an exampleNumerical modelling is an essential approach to understanding the behavior of thermal plasmas in various industrial applications. We propose a deep learning method for solving the partial…Linlin Zhong, Qi Gu, Bingyu Wu·Mar 17, 2020SaveLearn
Wigner-Smith Time Delay Matrix for Electromagnetics: Theory and PhenomenologyWigner-Smith (WS) time delay concepts have been used extensively in quantum mechanics to characterize delays experienced by particles interacting with a potential well. This paper formally extends WS…Utkarsh R. Patel, Eric Michielssen·Mar 16, 2020SaveLearn
High-order arbitrary Lagrangian-Eulerian discontinuous Galerkin methods for the incompressible Navier-Stokes equationsThis paper presents robust discontinuous Galerkin methods for the incompressible Navier-Stokes equations on moving meshes. High-order accurate arbitrary Lagrangian-Eulerian formulations are proposed…Niklas Fehn, Johannes Heinz, Wolfgang A. Wall et al.·Mar 16, 2020SaveLearn
Nonlinear eigenvalue problems for coupled Helmholtz equations modeling gradient-index graphene waveguidesWe discuss a quartic eigenvalue problem arising in the context of an optical waveguiding problem involving atomically thick 2D materials. The waveguide configuration we consider consists of a…Jung Heon Song, Matthias Maier, Mitchell Luskin·Mar 14, 2020SaveLearn
Unusual Intralayer Ferromagnetism Between S = 5/2 ions in MnBi2Te4: Role of Empty Bi p StatesThe layered magnetic topological insulator MnBi2Te4 is a promising platform to realize the quantum anomalous Hall effect because its layers possess intrinsic ferromagnetism. However, it is not…Jing Li, J. Y. Ni, X. Y. Li et al.·Mar 14, 2020SaveLearn
A random-batch Monte Carlo method for many-body systems with singular kernelsWe propose a fast potential splitting Markov Chain Monte Carlo method which costs O(1) time each step for sampling from equilibrium distributions (Gibbs measures) corresponding to particle systems…Lei Li, Zhenli Xu, Yue Zhao·Mar 14, 2020SaveLearn
Variational and Diffusion Quantum Monte Carlo Calculations with the CASINO CodeWe present an overview of the variational and diffusion quantum Monte Carlo methods as implemented in the CASINO program. We particularly focus on developments made in the last decade, describing…R. J. Needs, M. D. Towler, N. D. Drummond et al.·Mar 13, 2020SaveLearn
Performance portability of lattice Boltzmann methods for two-phase flows with phase changeNumerical codes using the Lattice Boltzmann Methods (LBM) for simulating one- or two-phase flows are widely compiled and run on graphical process units. However, those computational units necessitate…Werner Verdier, Pierre Kestener, Alain Cartalade·Mar 13, 2020SaveLearn
NSFnets (Navier-Stokes Flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equationsWe employ physics-informed neural networks (PINNs) to simulate the incompressible flows ranging from laminar to turbulent flows. We perform PINN simulations by considering two different formulations…Xiaowei Jin, Shengze Cai, Hui Li et al.·Mar 13, 2020SaveLearn
A General Approach to Seismic Inversion with Automatic DifferentiationImaging Earth structure or seismic sources from seismic data involves minimizing a target misfit function, and is commonly solved through gradient-based optimization. The adjoint-state method has…Weiqiang Zhu, Kailai Xu, Eric Darve et al.·Mar 12, 2020SaveLearn
Cell Lists Method Based on Doubly Linked Lists for Monte Carlo SimulationA cell lists method based on doubly linked lists and with complexity O(N) is developed for particle deletion and insertion in reaction ensemble Monte Carlo simulation. Because the random move in…Shaoyun Wang, Chaohui Tong·Mar 12, 2020SaveLearn
The pseudoatomic orbital basis: electronic accuracy and soft-mode distortions in ABO3 perovskitesThe perovskite oxides are known to be susceptible to structural distortions over a long wavelength when compared to their parent cubic structures. From an ab initio simulation perspective, this…Jack S. Baker, Tsuyoshi Miyazki, David R. Bowler·Mar 11, 2020SaveLearn
Fast and stable determinant quantum Monte CarloWe assess numerical stabilization methods employed in fermion many-body quantum Monte Carlo simulations. In particular, we empirically compare various matrix decomposition and inversion schemes to…Carsten Bauer·Mar 11, 2020SaveLearn
Comparing mesoscopic models for dendritic growthWe present a quantitative benchmark of multiscale models for dendritic growth simulations. We focus on approaches based on phase-field, dendritic needle network, and grain envelope dynamics. As a…Damien Tourret, Laszlo Sturz, Alexandre Viardin et al.·Mar 11, 2020SaveLearn
Three-dimensional needle network model for dendritic growth with fluid flowWe present a first implementation of the Dendritic Needle Network (DNN) model for dendritic crystal growth in three dimensions including convective transport in the melt. The numerical solving of the…Thomas Isensee, Damien Tourret·Mar 11, 2020SaveLearn
Global Attention based Graph Convolutional Neural Networks for Improved Materials Property PredictionMachine learning (ML) methods have gained increasing popularity in exploring and developing new materials. More specifically, graph neural network (GNN) has been applied in predicting material…Steph-Yves Louis, Yong Zhao, Alireza Nasiri et al.·Mar 11, 2020SaveLearn
On the Impact of Fluid Structure Interaction in Blood Flow Simulations: Stenotic Coronary Artery BenchmarkWe study the impact of using fluid-structure interactions (FSI) to simulate blood flow in a large stenosed artery. We compare typical flow configurations using Navier-Stokes in a rigid geometry…Lukas Failer, Piotr Minakowski, Thomas Richter·Mar 11, 2020SaveLearn
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental SystemsThere is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with…Jared Willard, Xiaowei Jia, Shaoming Xu et al.·Mar 10, 2020SaveLearn
Massively parallel simulations for disordered systemsSimulations of systems with quenched disorder are extremely demanding, suffering from the combined effect of slow relaxation and the need of performing the disorder average. As a consequence, new…Ravinder Kumar, Jonathan Gross, Wolfhard Janke et al.·Mar 10, 2020SaveLearn
Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural NetworksIn this paper, we introduce interpretable Siamese Neural Networks (SNN) for similarity detection to the field of theoretical physics. More precisely, we apply SNNs to events in special relativity,…Sebastian J. Wetzel, Roger G. Melko, Joseph Scott et al.·Mar 9, 2020SaveLearn
Factorized Machine Learning for Performance Modeling of Massively Parallel Heterogeneous Physical SimulationsWe demonstrate neural-network runtime prediction for complex, many-parameter, massively parallel, heterogeneous-physics simulations running on cloud-based MPI clusters. Because individual simulations…Ardavan Oskooi, Christopher Hogan, Alec M. Hammond et al.·Mar 9, 2020SaveLearn