On the weak scaling of the contact distance between two fluctuating interfaces with system sizeA pair of flat parallel surfaces, each freely diffusing along the direction of their separation, will eventually come into contact. If the shapes of these surfaces also fluctuate, then contact will…Clemens Moritz, Marcello Sega, Max Innerbichler et al.·Aug 20, 2020SaveLearn
Modeling flexoelectricity in soft dielectrics at finite deformationThis paper develops the equilibrium equations describing the flexoelectric effect in soft dielectrics under large deformations. Previous works have developed related theories using a flexoelectric…David Codony, Prakhar Gupta, Onofre Marco et al.·Aug 20, 2020SaveLearn
KS-pies: Kohn-Sham Inversion ToolkitA Kohn-Sham (KS) inversion determines a KS potential and orbitals corresponding to a given electron density, a procedure that has applications in developing and evaluating functionals used in density…Seungsoo Nam, Ryan J. McCarty, Hansol Park et al.·Aug 20, 2020SaveLearn
Studying the potential of Graphcore IPUs for applications in Particle PhysicsThis paper presents the first study of Graphcore's Intelligence Processing Unit (IPU) in the context of particle physics applications. The IPU is a new type of processor optimised for machine…Lakshan Ram Madhan Mohan, Alexander Marshall, Samuel Maddrell-Mander et al.·Aug 20, 2020SaveLearn
Real-Time Time-Dependent Density Functional Theory within FHI-aimsReal-Time Time-Dependent Density Functional Theory (TDDFT) has become an attractive tool to model quantum dynamics on a first-principles Density Functional Theory level. In recent years, several…Joscha Hekele, Peter Kratzer·Aug 20, 2020SaveLearn
Data-Driven Solvers for Strongly Nonlinear Material ResponseThis work presents a data-driven magnetostatic finite-element solver that is specifically well-suited to cope with strongly nonlinear material responses. The data-driven computing framework is…Armin Galetzka, Dimitrios Loukrezis, Herbert De Gersem·Aug 19, 2020SaveLearn
Predicting ground state configuration of energy landscape ensemble using graph neural networkMany scientific problems seek to find the ground state in a rugged energy landscape, a task that becomes prohibitively difficult for large systems. Within a particular class of problems, however, the…Seong Ho Pahng, Michael P. Brenner·Aug 19, 2020SaveLearn
Training machine-learning potentials for crystal structure prediction using disordered structuresPrediction of the stable crystal structure for multinary (ternary or higher) compounds with unexplored compositions demands fast and accurate evaluation of free energies in exploring the vast…Changho Hong, Jeong Min Choi, Wonseok Jeong et al.·Aug 18, 2020SaveLearn
Efficient planning of peen-forming patterns via artificial neural networksRobust automation of the shot peen forming process demands a closed-loop feedback in which a suitable treatment pattern needs to be found in real-time for each treatment iteration. In this work, we…Wassime Siguerdidjane, Farbod Khameneifar, Frédérick P. Gosselin·Aug 18, 2020SaveLearn
A port-Hamiltonian approach to modeling the structural dynamics of complex systemsWith this contribution, we give a complete and comprehensive framework for modeling the dynamics of complex mechanical structures as port-Hamiltonian systems. This is motivated by research on the…Alexander Warsewa, Michael Böhm, Oliver Sawodny et al.·Aug 18, 2020SaveLearn
A plane wave study on the localized-extended transitions in the one-dimensional incommensurate systemsBased on our recently proposed plane wave framework, we theoretically study the localized-extended transition in the one dimensional incommensurate systems with cosine type of potentials, which are…Huajie Chen, Aihui Zhou, Yuzhi Zhou·Aug 18, 2020SaveLearn
A sample-based stochastic finite element method for structural reliability analysisThis paper presents a new methodology for structural reliability analysis via stochastic finite element method (SFEM). A novel sample-based SFEM is firstly used to compute structural stochastic…Zhibao Zheng·Aug 18, 2020SaveLearn
A hybrid eikonal solver for accurate first-arrival traveltime computation in anisotropic media with strong contrastsFirst-arrival traveltime computation is crucial for many applications such as traveltime tomography, Kirchhoff migration, etc. There exist two major issues in conventional eikonal solvers: the source…Kai Gao, Lianjie Huang·Aug 18, 2020SaveLearn
Finite Element Network Analysis: A Machine Learning based Computational Framework for the Simulation of Physical SystemsThis study introduces the concept of finite element network analysis (FENA) which is a physics-informed, machine-learning-based, computational framework for the simulation of complex physical…Mehdi Jokar, Fabio Semperlotti·Aug 17, 2020SaveLearn
Machine-learning-based sampling method for exploring local energy minima of interstitial species in a crystalAn efficient machine-learning-based method combined with a conventional local optimization technique has been proposed for exploring local energy minima of interstitial species in a crystal. In the…Kazuaki Toyoura, Kansei Kanayama·Aug 17, 2020SaveLearn
Projection-based Implicit Modeling Method (PIMM) for Functionally Graded Lattice OptimizationThis paper proposes a projection-based implicit modeling method (PIMM) for functionally graded lattice optimization, which does not require any homogenization techniques. In this method, a parametric…Hao Deng, Albert C. To·Aug 17, 2020SaveLearn
Simulation of plasma accelerators with the Particle-In-Cell methodWe present the standard electromagnetic Particle-in-Cell method, starting from the discrete approximation of derivatives on a uniform grid. The application to second-order, centered,…J. L. Vay·Aug 17, 2020SaveLearn
An accurate hyper-singular boundary integral equation method for dynamic poroelasticity in two dimensionsThis paper is concerned with the boundary integral equation method for solving the exterior Neumann boundary value problem of dynamic poroelasticity in two dimensions. The main contribution of this…Lu Zhang, Liwei Xu, Tao Yin·Aug 17, 2020SaveLearn
A new set of efficient SMP-parallel 2D Fourier subroutinesExtensive set of tests on different platforms indicated that there is a performance drop of current standard de facto software library for the Discrete Fourier Transform (DFT) in case of large 2D…Alexander O. Korotkevich·Aug 17, 2020SaveLearn
Elmer FEM-Dakota: A unified open-source computational framework for electromagnetics and data analyticsOpen-source electromagnetic design software, Elmer FEM, was interfaced with data analytics toolkit, Dakota. Furthermore, the coupled software was validated against a benchmark test. The interface…Anjali Sandip·Aug 16, 2020SaveLearn
Statistical Mechanics of the L-Distance Minimal Dominating Set problemStatistical mechanics is widely applied to solve hard optimization problem, the optimal strategy related to ground state energy that depends on low temperature. Common thermodynamic process is…Yusupjan Habibulla·Aug 16, 2020SaveLearn
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimizationAdjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability…S. Ashwin Renganathan, Romit Maulik and, Jai Ahuja·Aug 15, 2020SaveLearn
Error-Controlled Hybrid Adaptive Fast Solver for Regularized Vortex MethodsIn this paper, an error-controlled hybrid adaptive fast solver that combine both O(N) and O(N log N) scheme is proposed. For a given accuracy, the adaptive solver is used in the context of…Samer Salloum, Issam Lakkis·Aug 15, 2020SaveLearn
Orbital Graph Convolutional Neural Network for Material Property PredictionMaterial representations that are compatible with machine learning models play a key role in developing models that exhibit high accuracy for property prediction. Atomic orbital interactions are one…Mohammadreza Karamad, Rishikesh Magar, Yuting Shi et al.·Aug 14, 2020SaveLearn
Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical SystemsIndustrial dynamical systems often exhibit multi-scale response due to material heterogeneities, operation conditions and complex environmental loadings. In such problems, it is the case that the…Waad Subber, Sayan Ghosh, Piyush Pandita et al.·Aug 14, 2020SaveLearn