Linear stability of athermal regularized lattice Boltzmann methodsThe present work is dedicated to a better understanding of the stability properties of regularized lattice Boltzmann (LB) schemes. To this extent, linear stability analyses of two-dimensional models…Gauthier Wissocq, Christophe Coreixas, Jean-François Boussuge·Jun 12, 2020SaveLearn
Uncertainty Quantification in Classical Molecular DynamicsMolecular dynamics simulation is now a widespread approach for understanding complex systems on the atomistic scale. It finds applications from physics and chemistry to engineering, life and medical…Shunzhou Wan, Robert C. Sinclair, Peter V. Coveney·Jun 12, 2020SaveLearn
Parametric solutions of turbulent incompressible flows in OpenFOAM via the proper generalised decompositionAn a priori reduced order method based on the proper generalised decomposition (PGD) is proposed to compute parametric solutions involving turbulent incompressible flows of interest in an industrial…Vasileios Tsiolakis, Matteo Giacomini, Ruben Sevilla et al.·Jun 12, 2020SaveLearn
Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field dataWe propose a customized convolutional neural network based autoencoder called a hierarchical autoencoder, which allows us to extract nonlinear autoencoder modes of flow fields while preserving the…Kai Fukami, Taichi Nakamura, Koji Fukagata·Jun 12, 2020SaveLearn
Conformable Fractional Isothermal Gas SpheresThe isothermal gas sphere is well known as a powerful tool to model many problems in astrophysics, physics, chemistry, and engineering. This singular differential equation has not an exact solution…Eltayeb A. Yousif, Ahmed M. A. Adam, Abaker A. Hassaballa1 et al.·Jun 12, 2020SaveLearn
Analysis, Design, and Generalization of Electrochemical Impedance Spectroscopy (EIS) Inversion AlgorithmsWe introduce a framework for analyzing and designing EIS inversion algorithms. Our framework stems from the observation of four features common to well-defined EIS inversion algorithms, namely (1)…Surya Effendy, Juhyun Song, Martin Z. Bazant·Jun 12, 2020SaveLearn
Bayesian optimization for inverse problems in time-dependent quantum dynamicsWe demonstrate an efficient algorithm for inverse problems in time-dependent quantum dynamics based on feedback loops between Hamiltonian parameters and the solutions of the Schrödinger equation. Our…Z. Deng, I. Tutunnikov, I. Sh. Averbukh et al.·Jun 11, 2020SaveLearn
Quantum inspired K-means algorithm using matrix product statesMatrix product state has become the algorithm of choice when studying one-dimensional interacting quantum many-body systems, which demonstrates to be able to explore the most relevant portion of the…Xiao Shi, Yun Shang, Chu Guo·Jun 11, 2020SaveLearn
A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data using Unstructured Spatial DiscretizationsWe propose a nonlinear manifold learning technique based on deep convolutional autoencoders that is appropriate for model order reduction of physical systems in complex geometries. Convolutional…John Tencer, Kevin Potter·Jun 11, 2020SaveLearn
Algorithm for generating irreducible site-occupancy configurationsGenerating irreducible site-occupancy configurations by taking advantage of crystal symmetry is a ubiquitous method for accelerating of disordered structure prediction, which plays an important role…Ji-Chun Lian, Hong-Yu Wu, Wei-Qing Huang et al.·Jun 11, 2020SaveLearn
GEOM: Energy-annotated molecular conformations for property prediction and molecular generationMachine learning (ML) outperforms traditional approaches in many molecular design tasks. ML models usually predict molecular properties from a 2D chemical graph or a single 3D structure, but neither…Simon Axelrod, Rafael Gomez-Bombarelli·Jun 9, 2020SaveLearn
Non-Gaussian distribution of displacements for Lennard-Jones particles in equilibriumMost meso-scale simulation methods assume Gaussian distributions of velocity-like quantities. These quantities are not true velocities, however, but rather time-averaged velocities or displacements…Aleksandra Pachalieva, Alexander J. Wagner·Jun 9, 2020SaveLearn
retQSS: A Novel Methodology for Efficient Modeling and Simulation of Particle Systems in Reticulated GeometriesThis work presents retQSS, a novel methodology for efficient modeling and simulation of particle systems in reticulated meshed geometries. On the simulation side, retQSS profits from the…Lucio Santi, Joaquín Fernández, Ernesto Kofman et al.·Jun 9, 2020SaveLearn
Report from the A.I. For Nuclear Physics WorkshopThis report is an outcome of the workshop "AI for Nuclear Physics" held at Thomas Jefferson National Accelerator Facility on March 4-6, 2020. The workshop brought together 184 scientists to…Paulo Bedaque, Amber Boehnlein, Mario Cromaz et al.·Jun 9, 2020SaveLearn
Simple and efficient algorithms for training machine learning potentials to force dataAbstract Machine learning models, trained on data from ab initio quantum simulations, are yielding molecular dynamics potentials with unprecedented accuracy. One limiting factor is the quantity of…Justin S. Smith, Nicholas Lubbers, Aidan P. Thompson et al.·Jun 9, 2020SaveLearn
Fast Modeling and Understanding Fluid Dynamics Systems with Encoder-Decoder NetworksIs a deep learning model capable of understanding systems governed by certain first principle laws by only observing the system's output? Can deep learning learn the underlying physics and honor…Rohan Thavarajah, Xiang Zhai, Zheren Ma et al.·Jun 9, 2020SaveLearn
Benchmark Computation of Morphological Complexity in the Functionalized Cahn-Hilliard Gradient FlowReductions of the self-consistent mean field theory model of amphiphilic molecules in solvent can lead to a singular family of functionalized Cahn-Hilliard energies. We modify these energies,…Andrew Christlieb, Keith Promislow, Zengqiang Tan et al.·Jun 8, 2020SaveLearn
Multi-fidelity Generative Deep Learning Turbulent FlowsIn computational fluid dynamics, there is an inevitable trade off between accuracy and computational cost. In this work, a novel multi-fidelity deep generative model is introduced for the surrogate…Nicholas Geneva, Nicholas Zabaras·Jun 8, 2020SaveLearn
Data-driven topology design using a deep generative modelIn this paper, we propose a sensitivity-free and multi-objective structural design methodology called data-driven topology design. It is schemed to obtain high-performance material distributions from…Shintaro Yamasaki, Kentaro Yaji, Kikuo Fujita·Jun 8, 2020SaveLearn
Schr\"odinger PCA: On the Duality between Principal Component Analysis and Schr\"odinger EquationPrincipal component analysis (PCA) has achieved great success in unsupervised learning by identifying covariance correlations among features. If the data collection fails to capture the covariance…Ziming Liu, Sitian Qian, Yixuan Wang et al.·Jun 8, 2020SaveLearn
Improved Recursive Computation of Clebsch-Gordan CoefficientsFast, accurate, and stable computation of the Clebsch-Gordan (C-G) coefficients is always desirable, for example, in light scattering simulations, the translation of the multipole fields, quantum…Guanglang Xu·Jun 7, 2020SaveLearn
Neural Vortex Method: from Finite Lagrangian Particles to Infinite Dimensional Eulerian DynamicsIn the field of fluid numerical analysis, there has been a long-standing problem: lacking of a rigorous mathematical tool to map from a continuous flow field to discrete vortex particles, hurdling…Shiying Xiong, Xingzhe He, Yunjin Tong et al.·Jun 7, 2020SaveLearn
RoeNets: Predicting Discontinuity of Hyperbolic Systems from Continuous DataWe introduce Roe Neural Networks (RoeNets) that can predict the discontinuity of the hyperbolic conservation laws (HCLs) based on short-term discontinuous and even continuous training data. Our…Shiying Xiong, Xingzhe He, Yunjin Tong et al.·Jun 7, 2020SaveLearn
An Overview of Computational Fluid Structure Interaction: Methods and ApplicationsOver the past few decades, there has been a rapid improvement in computational power as well as techniques to simulate the real world phenomenon which has enabled us to understand the physics and…Sumant R Morab, Atul Sharma·Jun 7, 2020SaveLearn
Accurately Solving Physical Systems with Graph LearningIterative solvers are widely used to accurately simulate physical systems. These solvers require initial guesses to generate a sequence of improving approximate solutions. In this contribution, we…Han Shao, Tassilo Kugelstadt, Torsten Hädrich et al.·Jun 6, 2020SaveLearn