Simulating diffusion properties of solid-state electrolytes via a neural network potential: Performance and training schemeThe recently published DeePMD model (https://github.com/deepmodeling/deepmd-kit), based on a deep neural network architecture, brings the hope of solving the time-scale issue which often prevents the…Aris Marcolongo, Tobias Binninger, Federico Zipoli et al.·Oct 22, 2019SaveLearn
Coercing Machine Learning to Output Physically Accurate ResultsMany machine/deep learning artificial neural networks are trained to simply be interpolation functions that map input variables to output values interpolated from the training data in a…Zhenglin Geng, Dan Johnson, Ronald Fedkiw·Oct 21, 2019SaveLearn
Extracting local switching fields in permanent magnets using machine learningMicrostructural features play an important role for the quality of permanent magnets. The coercivity is greatly influenced by crystallographic defects, which is well known for MnAl-C, for example. In…Markus Gusenbauer, Harald Oezelt, Johann Fischbacher et al.·Oct 21, 2019SaveLearn
Effective energy density determines the dynamics of suspensions of active and passive matterThe unique properties of suspensions containing both active (self-propelling) and passive matter, arising from the nonequilibrium nature of these systems, have been widely studied (e.g., enhanced…Ryan Krafnic, Angel E. Garcia·Oct 21, 2019SaveLearn
Non-uniqueness of the Quasinormal Mode Expansion of Electromagnetic Lorentz Dispersive MaterialsAny optical structure possesses resonance modes and its response to an excitation can be decomposed onto the quasinormal and numerical modes of discretized Maxwell's operator. In this paper, we…Alexandre Gras, Philippe Lalanne, Marc Duruflé·Oct 21, 2019SaveLearn
Learning and Meta-Learning of Stochastic Advection-Diffusion-Reaction Systems from Sparse MeasurementsPhysics-informed neural networks (PINNs) were recently proposed in [1] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution while a…Xiaoli Chen, Jinqiao Duan, George Em Karniadakis·Oct 21, 2019SaveLearn
Solving Fokker-Planck equation using deep learningThe probability density function of stochastic differential equations is governed by the Fokker-Planck (FP) equation. A novel machine learning method is developed to solve the general FP equations…Yong Xu, Hao Zhang, Yongge Li et al.·Oct 20, 2019SaveLearn
Laser ablation in liquid: bridge from a plasma stage to bubble formationLaser ablation through liquid is an important process that have to be studied for applications which use laser ablation in liquid (LAL) and laser shock peening (LSP). LAL is employed for production…N. A. Inogamov, V. A. Khokhlov, Yu. V. Petrov et al.·Oct 20, 2019SaveLearn
Poisson CNN: Convolutional neural networks for the solution of the Poisson equation on a Cartesian meshThe Poisson equation is commonly encountered in engineering, for instance in computational fluid dynamics (CFD) where it is needed to compute corrections to the pressure field to ensure the…Ali Girayhan Özbay, Arash Hamzehloo, Sylvain Laizet et al.·Oct 18, 2019SaveLearn
Numerical Precision Effects on GPU Simulation of Massive Spatial Data, Based on the Modified Planar Rotator ModelThe present research builds on a recently proposed spatial prediction method for discretized two-dimensional data, based on a suitably modified planar rotator (MPR) spin model from statistical…Matúš Lach, Michal Borovský, Milan Žukovič·Oct 18, 2019SaveLearn
Efficient Schmidt number scaling in dissipative particle dynamicsDissipative particle dynamics is a widely used mesoscale technique for the simulation of hydrodynamics (as well as immersed particles) utilizing coarse-grained molecular dynamics. While the method is…Ryan C. Krafnick, Angel E. Garcia·Oct 18, 2019SaveLearn
Burnett Spectral Method for High-Speed Rarefied Gas FlowsWe introduce a numerical solver for the spatially inhomogeneous Boltzmann equation using the Burnett spectral method. The modelling and discretization of the collision operator are based on the…Zhicheng Hu, Zhenning Cai·Oct 18, 2019SaveLearn
Acceleration techniques for semiclassical Maxwell-Bloch systems: An application to discrete quantum dot ensemblesThe solution to Maxwell-Bloch systems using an integral-equation-based framework has proven effective at capturing collective features of laser-driven and radiation-coupled quantum dots, such as…C. Glosser, E. Lu, T. J. Bertus et al.·Oct 18, 2019SaveLearn
Efficient and Scalable Approach to Equilibrium Conditional Simulation of Gibbs Markov Random FieldsWe study the performance of an automated hybrid Monte Carlo (HMC) approach for conditional simulation of a recently proposed, single-parameter Gibbs Markov random field (Gibbs MRF). The MRF is based…Milan Žukovič, Dionissios T. Hristopulos·Oct 18, 2019SaveLearn
Algorithms for uniform particle initialization in domains with complex boundariesAccurate mesh-free simulation of fluid flows involving complex boundaries requires that the boundaries be captured accurately in terms of particles. In the context of…Pawan Negi, Prabhu Ramachandran·Oct 17, 2019SaveLearn
adcc: A versatile toolkit for rapid development of algebraic-diagrammatic construction methodsADC-connect (adcc) is a hybrid python/C++ module for performing excited state calculations based on the algebraic-diagrammatic construction scheme for the polarisation propagator (ADC). Key design…Michael F. Herbst, Maximilian Scheurer, Thomas Fransson et al.·Oct 17, 2019SaveLearn
Computationally Efficient CFD Prediction of Bubbly Flow using Physics-Guided Deep LearningTo realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale framework was proposed in this paper by applying a physics-guided data-driven approach.…Han Bao, Jinyong Feng, Nam Dinh et al.·Oct 17, 2019SaveLearn
Lattice Propagators and Haldane-Wu Fractional StatisticsWe point out a formal analogy between lattice kinetic propagators and Haldane-Wu fractional statistics. The analogy could be used to compute the partition function of fractional quantum systems by…Sauro Succi, Marco Lauricella·Oct 17, 2019SaveLearn
Visualizing the world's largest turbulence simulationIn this exploratory submission we present the visualization of the largest interstellar turbulence simulations ever performed, unravelling key astrophysical processes concerning the formation of…Salvatore Cielo, Luigi Iapichino, Johannes Günther et al.·Oct 17, 2019SaveLearn
Numerical Considerations for Advection-Diffusion Problems in Cardiovascular HemodynamicsNumerical simulations of cardiovascular mass transport pose significant challenges due to the wide range of Péclet numbers and backflow at Neumann boundaries. In this paper we present and discuss…Sabrina R. Lynch, Nitesh Nama, Zelu Xu et al.·Oct 16, 2019SaveLearn
Fast general two- and three-body interatomic potentialWe introduce a new class of machine learning interatomic potentials - fast General Two- and Three-body Potential (GTTP), which is as fast as conventional empirical potentials and require…Sergey Pozdnyakov, Artem R. Oganov, Efim Mazhnik et al.·Oct 16, 2019SaveLearn
Deep Coregionalization for the Emulation of Spatial-Temporal FieldsData-driven surrogate models are widely used for applications such as design optimization and uncertainty quantification, where repeated evaluations of an expensive simulator are required. For most…Wei Xing, Robert M. Kirby, Shandian Zhe·Oct 16, 2019SaveLearn
Orbital-dependent backflow wave functions for real-space quantum Monte CarloWe present and motivate an efficient way to include orbital dependent many--body correlations in trial wave function of real--space Quantum Monte Carlo methods for use in electronic structure…Markus Holzmann, Saverio Moroni·Oct 16, 2019SaveLearn
A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulenceIn the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the a priori settings. These models utilize resolved…Suraj Pawar, Omer San, Adil Rasheed et al.·Oct 16, 2019SaveLearn
On preconditioning the self-consistent field iteration in real-space Density Functional TheoryWe present a real-space formulation for isotropic Fourier-space preconditioners used to accelerate the self-consistent field iteration in Density Functional Theory calculations. Specifically, after…Shashikant Kumar, Qimen Xu, Phanish Suryanarayana·Oct 15, 2019SaveLearn