A Kernel-based Machine Learning Approach to Computing Quasiparticle Energies within Many-Body Green's Functions TheoryWe present a Kernel Ridge Regression (KRR) based supervised learning method combined with Genetic Algorithms (GAs) for the calculation of quasiparticle energies within Many-Body Green's Functions…Gianluca Tirimbó, Onur Çaylak, Björn Baumeier·Dec 3, 2020SaveLearn
Compositional Effects in Thermal, Compositional and Reactive SimulationThis work studies the influence of several compositional effects on thermal and reactive processes. First, the impact of using a fully compositional model in the context of thermal simulations is…Matthias A. Cremon, Margot G. Gerritsen·Dec 3, 2020SaveLearn
Deploying deep learning in OpenFOAM with TensorFlowWe outline the development of a data science module within OpenFOAM which allows for the in-situ deployment of trained deep learning architectures for general-purpose predictive tasks. This module is…Romit Maulik, Himanshu Sharma, Saumil Patel et al.·Dec 1, 2020SaveLearn
Temperature-steerable flowsBoltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples of a physical system's equilibrium…Manuel Dibak, Leon Klein, Frank Noé·Dec 1, 2020SaveLearn
On numerical broadening of particle size spectra: a condensational growth study using PyMPDATAThis work discusses the numerical aspects of representing the diffusional (condensational) growth in particulate systems such as atmospheric clouds. It focuses on the Eulerian modeling approach, in…Michael Olesik, Jakub Banaśkiewicz, Piotr Bartman et al.·Nov 30, 2020SaveLearn
Spectral quadrature for the first principles study of crystal defects: Application to magnesiumWe present an accurate and efficient finite-difference formulation and parallel implementation of Kohn-Sham Density (Operator) Functional Theory (DFT) for non periodic systems embedded in a bulk…Swarnava Ghosh, Kaushik Bhattacharya·Nov 27, 2020SaveLearn
Perturbation theory for Maxwell's equations in anisotropic materials with shifting boundariesPerturbation theory is a kind of estimation method based on theorem of Taylor expansion, and is useful to investigate electromagnetic solutions of small changes. By considering a sharp boundary as a…Di Yu, Xiaomin Lv, Boyu Fan et al.·Nov 26, 2020SaveLearn
A mass-energy-conserving discontinuous Galerkin scheme for the isotropic multispecies Rosenbluth--Fokker--Planck equationStructure-preserving discretization of the Rosenbluth-Fokker-Planck equation is still an open question especially for unlike-particle collision. In this paper, a mass-energy-conserving isotropic…Takashi Shiroto, Akinobu Matsuyama, Nobuyuki Aiba et al.·Nov 26, 2020SaveLearn
Tensor-structured algorithm for reduced-order scaling large-scale Kohn-Sham density functional theory calculationsWe present a tensor-structured algorithm for efficient large-scale DFT calculations by constructing a Tucker tensor basis that is adapted to the Kohn-Sham Hamiltonian and localized in real-space. The…Chih-Chuen Lin, Phani Motamarri, Vikram Gavini·Nov 24, 2020SaveLearn
Learning the ground state of a non-stoquastic quantum Hamiltonian in a rugged neural network landscapeStrongly interacting quantum systems described by non-stoquastic Hamiltonians exhibit rich low-temperature physics. Yet, their study poses a formidable challenge, even for state-of-the-art numerical…Marin Bukov, Markus Schmitt, Maxime Dupont·Nov 23, 2020SaveLearn
Numerical quasi-conformal transformations for electron dynamics on strained graphene surfacesThe dynamics of low energy electrons in general static strained graphene surface is modelled mathematically by the Dirac equation in curved space-time. In Cartesian coordinates, a parametrization of…F. Fillion-Gourdeau, E. Lorin, S. MacLean·Nov 20, 2020SaveLearn
Simulation of 2D ballistic deposition of porous nanostructured thin-filmsA "two-dimensional ballistic deposition" (2D-BD) code has been developed to study the geometric effects in ballistic deposition of thin-film growth. Circular discs are used as depositing specie to…S. Bukkuru, H. Hemani, S. M. Haque et al.·Nov 20, 2020SaveLearn
Tensile properties of structural I clathrate hydrates:Role of guest-host hydrogen bonding abilityClathrate hydrates (CHs) are one of the most promising molecular structures in applications of gas capture and storage, and gas separations. Fundamental knowledge of mechanical characteristics of CHs…Yue Xin, Qiao Shi, Ke Xu et al.·Nov 19, 2020SaveLearn
A remeshed vortex method for mixed rigid/soft body fluid--structure interactionWe outline a 2D algorithm for solving incompressible flow--structure interaction problems for mixed rigid/soft body representations, within a consistent framework based on the remeshed vortex method.…Yashraj Bhosale, Tejaswin Parthasarathy, Mattia Gazzola·Nov 19, 2020SaveLearn
Phase-field modeling of multivariant martensitic transformation at finite-strain: computational aspects and large-scale finite-element simulationsLarge-scale 3D martensitic microstructure evolution problems are studied using a finite-element discretization of a finite-strain phase-field model. The model admits an arbitrary crystallography of…K. Tůma, M. Rezaee-Hajidehi, J. Hron et al.·Nov 17, 2020SaveLearn
Variation Free Approach for Molecular Density Functional Theory: Data-driven Stochastic OptimizationDensity functional theory (DFT) is an efficient instrument for describing a wide range of nanoscale phenomena: wetting transition, capillary condensation, adsorption, etc. In this paper, we suggest a…Yuriy Kanygin, Irina Nesterova, Pavel Lomovitskiy et al.·Nov 17, 2020SaveLearn
An Interpretable Machine Learning Model for Deformation of Multi-Walled Carbon NanotubesWe present a novel interpretable machine learning model to accurately predict complex rippling deformations of Multi-Walled Carbon Nanotubes(MWCNTs) made of millions of atoms. Atomistic-physics-based…Upendra yadav, Shashank Pathrudkar, Susanta Ghosh·Nov 16, 2020SaveLearn
Multi-species modeling in the particle-based ESBGK method for monatomic gas speciesMulti-species modeling is implemented for the particle-based ellipsoidal statistical Bhatnagar-Gross-Krook (ESBGK) for monatomic species in the open-source plasma simulation suite PICLas. After a…Marcel Pfeiffer, Asim Mirza, Paul Nizenkov·Nov 15, 2020SaveLearn
Learning a Reduced Basis of Dynamical Systems using an AutoencoderMachine learning models have emerged as powerful tools in physics and engineering. Although flexible, a fundamental challenge remains on how to connect new machine learning models with known physics.…David Sondak, Pavlos Protopapas·Nov 14, 2020SaveLearn
Better, Faster Fermionic Neural NetworksThe Fermionic Neural Network (FermiNet) is a recently-developed neural network architecture that can be used as a wavefunction Ansatz for many-electron systems, and has already demonstrated high…James S. Spencer, David Pfau, Aleksandar Botev et al.·Nov 13, 2020SaveLearn
Natively Periodic Fast Multipole Method: Approximating the Optimal Green FunctionThe Fast Multipole Method (FMM) obeys periodic boundary conditions "natively" if it uses a periodic Green function for computing the multipole expansion in the interaction zone of each FMM oct-tree…Nickolay Y. Gnedin·Nov 13, 2020SaveLearn
Heuristic Methods and Performance Bounds for Photonic DesignIn the photonic design problem, a scientist or engineer chooses the physical parameters of a device to best match some desired device behavior. Many instances of the photonic design problem can be…Guillermo Angeris, Jelena Vučković, Stephen Boyd·Nov 12, 2020SaveLearn
The Bayesian Committee Approach for Computational Physics ProblemsIn this work, we propose a method for efficient learning of a multi-dimensional function. This method combines the Bayesian neural networks and the query-by-committee method. A committee made of deep…Li Chen, Xiao Liang, Hui Zhai·Nov 12, 2020SaveLearn
A high-order / low-order (HOLO) algorithm for preserving conservation in time-dependent low-rank transport calculationsDynamical low-rank (DLR) approximation methods have previously been developed for time-dependent radiation transport problems. One crucial drawback of DLR is that it does not conserve important…Zhuogang Peng, Ryan G. McClarren·Nov 11, 2020SaveLearn
Machine Learning for Magnetic Phase Diagrams and Inverse Scattering ProblemsMachine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including…Anjana M. Samarakoon, D. Alan Tennant·Nov 11, 2020SaveLearn