Training neural networks under physical constraints using a stochastic augmented Lagrangian approachWe investigate the physics-constrained training of an encoder-decoder neural network for approximating the Fokker-Planck-Landau collision operator in the 5-dimensional kinetic fusion simulation in…Alp Dener, Marco Andres Miller, Randy Michael Churchill et al.·Sep 15, 2020SaveLearn
Fixed Inducing Points Online Bayesian Calibration for Computer Models with an Application to a Scale-Resolving CFD SimulationThis paper proposes a novel fixed inducing points online Bayesian calibration (FIPO-BC) algorithm to efficiently learn the model parameters using a benchmark database. The standard Bayesian…Yu Duan, Matthew Eaton, Michael Bluck·Sep 15, 2020SaveLearn
Analysis of finite-volume discrete adjoint fields for two-dimensional compressible Euler flowsThis work deals with a number of questions relative to the discrete and continuous adjoint fields associated with the compressible Euler equations and classical aerodynamic functions. The consistency…Jacques Peter, Florent Renac, Clément Labbé·Sep 15, 2020SaveLearn
Spectral extended finite element method for band structure calculations in phononic crystalsIn this paper, we compute the band structure of one- and two-dimensional phononic composites using the extended finite element method (X-FEM) on structured higher-order (spectral) finite element…Eric B. Chin, Amir Ashkan Mokhtari, Ankit Srivastava et al.·Sep 15, 2020SaveLearn
FEM modelling techniques for simulation of 3D concrete printingThree-dimensional concrete printing (3DCP) has gained a lot of popularity in recent years. According to many, 3DCP is set to revolutionize the construction industry: yielding unparalleled aesthetics,…Gieljan Vantyghem, Ticho Ooms, Wouter De Corte·Sep 15, 2020SaveLearn
A machine learning approach for efficient multi-dimensional integrationWe propose a novel multi-dimensional integration algorithm using a machine learning (ML) technique. After training a ML regression model to mimic a target integrand, the regression model is used to…Boram Yoon·Sep 14, 2020SaveLearn
Development of a general-purpose machine-learning interatomic potential for aluminum by the physically-informed neural network methodAbstract Interatomic potentials constitute the key component of large-scale atomistic simulations of materials. The recently proposed physically-informed neural network (PINN) method combines a…G. P. Purja Pun, V. Yamakov, J. Hickman et al.·Sep 14, 2020SaveLearn
Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compressionFingerprint distances, which measure the similarity of atomic environments, are commonly calculated from atomic environment fingerprint vectors. In this work we present the simplex method which can…Behnam Parsaeifard, Daniele Tomerini, Deb Sankar De et al.·Sep 14, 2020SaveLearn
Unified Gas-kinetic Wave-Particle Method IV: Multi-species Gas Mixture and Plasma TransportIn this paper, we extend the unified gas-kinetic wave-particle (UGKWP) method to the multi-species gas mixture and multiscale plasma transport. The construction of the scheme is based on the direct…Chang Liu, Kun Xu·Sep 14, 2020SaveLearn
Accuracy, Stability, and Performance Comparison between the Spectral Difference and Flux Reconstruction SchemesWe report the development of a discontinuous spectral element flow solver that includes the implementation of both spectral difference and flux reconstruction formulations. With this high order…Christopher Cox, Will Trojak, Tarik Dzanic et al.·Sep 13, 2020SaveLearn
Atomistic and mean-field estimates of effective stiffness tensor of nanocrystalline materials of hexagonal symmetryAnisotropic core-shell model of a nano-grained polycrystal is extended to estimate the effective elastic stiffness of several metals of hexagonal crystal lattice symmetry. In the approach the bulk…Katarzyna Kowalczyk-Gajewska, Marcin Maździarz·Sep 12, 2020SaveLearn
Numerical investigation into coarse-scale models of diffusion in complex heterogeneous mediaComputational modelling of diffusion in heterogeneous media is prohibitively expensive for problems with fine-scale heterogeneities. A common strategy for resolving this issue is to decompose the…Nathan G. March, Elliot J. Carr, Ian W. Turner·Sep 12, 2020SaveLearn
Symplectic Gaussian Process Regression of Hamiltonian Flow MapsWe present an approach to construct appropriate and efficient emulators for Hamiltonian flow maps. Intended future applications are long-term tracing of fast charged particles in accelerators and…Katharina Rath, Christopher G. Albert, Bernd Bischl et al.·Sep 11, 2020SaveLearn
Direct prediction of phonon density of states with Euclidean neural networksMachine learning has demonstrated great power in materials design, discovery, and property prediction. However, despite the success of machine learning in predicting discrete properties, challenges…Zhantao Chen, Nina Andrejevic, Tess Smidt et al.·Sep 10, 2020SaveLearn
Adaptive Multidimensional Integration: VEGAS EnhancedWe describe a new algorithm, VEGAS+, for adaptive multidimensional Monte Carlo integration. The new algorithm adds a second adaptive strategy, adaptive stratified sampling, to the adaptive importance…G. Peter Lepage·Sep 10, 2020SaveLearn
Use the force! Reduced variance estimators for densities, radial distribution functions and local mobilities in molecular simulationsEven though the computation of local properties, such as densities or radial distribution functions, remains one of the most standard goals of molecular simulation, it still largely relies on…Benjamin Rotenberg·Sep 10, 2020SaveLearn
Approximate solution of two dimensional disc-like systems by one dimensional reduction: an approach through the Green function formalism using the Finite Elements MethodWe present a comprehensive study for common second order PDE's in two dimensional disk-like systems and show how their solution can be approximated by finding the Green function of an effective one…Alejandro Ferrero, Juan Pablo Mallarino·Sep 10, 2020SaveLearn
Hydration of NH4+ in Water: Bifurcated Hydrogen Bonding Structures and Fast Rotational DynamicsUnderstanding the hydration and diffusion of ions in water at the molecular level is a topic of widespread importance. The ammonium ion (NH4+) is an exemplar system that has received attention…Jianqing Guo, Liying Zhou, Andrea Zen et al.·Sep 10, 2020SaveLearn
Enhanced single-node boundary condition for the Lattice Boltzmann MethodWe propose a new way to implement Dirichlet boundary conditions for complex shapes using data from a single node only, in the context of the lattice Boltzmann method. The resulting novel method…Francesco Marson, Yann Thorimbert, Jonas Latt et al.·Sep 9, 2020SaveLearn
GPU-accelerated machine learning inference as a service for computing in neutrino experimentsMachine learning algorithms are becoming increasingly prevalent and performant in the reconstruction of events in accelerator-based neutrino experiments. These sophisticated algorithms can be…Michael Wang, Tingjun Yang, Maria Acosta Flechas et al.·Sep 9, 2020SaveLearn
Solving the k-sparse Eigenvalue Problem with Reinforcement LearningWe examine the possibility of using a reinforcement learning (RL) algorithm to solve large-scale eigenvalue problems in which the desired the eigenvector can be approximated by a sparse vector with…Li Zhou, Lihao Yan, Mark A. Caprio et al.·Sep 9, 2020SaveLearn
Combining data assimilation and machine learning to infer unresolved scale parametrisationIn recent years, machine learning (ML) has been proposed to devise data-driven parametrisations of unresolved processes in dynamical numerical models. In most cases, the ML training leverages…Julien Brajard, Alberto Carrassi, Marc Bocquet et al.·Sep 9, 2020SaveLearn
High-resolution three-dimensional crystalline microscopyIn this communication, we discuss how 3D information about the structure of a crystalline sample is encoded in Bragg 3DXCDI measurements. Our analysis brings to light the role of the experimental…Marc Allain, Virginie Chamard, Stephan O. Hruszkewycz·Sep 9, 2020SaveLearn
Scaling advantage of nonrelaxational dynamics for high-performance combinatorial optimizationThe development of physical simulators, called Ising machines, that sample from low energy states of the Ising Hamiltonian has the potential to drastically transform our ability to understand and…Timothee Leleu, Farad Khoyratee, Timothee Levi et al.·Sep 9, 2020SaveLearn
Machine learning topological invariants of non-Hermitian systemsThe study of topological properties by machine learning approaches has attracted considerable interest recently. Here we propose machine learning the topological invariants that are unique in…Ling-Feng Zhang, Ling-Zhi Tang, Zhi-Hao Huang et al.·Sep 9, 2020SaveLearn