Beam propagation simulation of phased laser arrays with atmospheric perturbationsDirected energy phased array (DEPA) systems have been proposed for novel applications such as beaming optical power for electrical use on remote sensors, rovers, spacecraft and future moon bases, as…Will Hettel, Peter Meinhold, Jonathan Y. Suen et al.·Jun 28, 2021SaveLearn
Chemulator: Fast, accurate thermochemistry for dynamical models through emulationChemical modelling serves two purposes in dynamical models: accounting for the effect of microphysics on the dynamics and providing observable signatures. Ideally, the former must be done as part of…J. Holdship, S. Viti, T. J. Haworth et al.·Jun 28, 2021SaveLearn
A unified multi-phase and multi-material formulation for combustion modellingThe motivation of this work is to produce an integrated formulation for material response due to detonation wave loading. Here, we focus on elastoplastic structural response. In particular, we are…Maria Nikodemou, Louisa Michael, Nikolaos Nikiforakis·Jun 28, 2021SaveLearn
Machine Learning S-Wave Scattering Phase Shifts Bypassing the Radial Schr\"odinger EquationWe present a proof of concept machine learning model resting on a convolutional neural network capable to yield accurate scattering s-wave phase shifts caused by different three-dimensional…Alessandro Romualdi, Gionni Marchetti·Jun 25, 2021SaveLearn
Energy balance and energy correction in dynamics of classical spin systemsEnergy-correction method is proposed as an addition to mainstream integrators for equations of motion of systems of classical spins. This solves the problem of non-conservation of energy in long…Dmitry A. Garanin·Jun 25, 2021SaveLearn
Code-Verification Techniques for the Method-of-Moments Implementation of the Electric-Field Integral EquationThe method-of-moments implementation of the electric-field integral equation yields many code-verification challenges due to the various sources of numerical error and their possible interactions.…Brian A. Freno, Neil R. Matula, Justin I. Owen et al.·Jun 25, 2021SaveLearn
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)Multifidelity simulation methodologies are often used in an attempt to judiciously combine low-fidelity and high-fidelity simulation results in an accuracy-increasing, cost-saving way. Candidates for…Michael Penwarden, Shandian Zhe, Akil Narayan et al.·Jun 25, 2021SaveLearn
On the Role of Atomic Binding Forces and Warm-Dense-Matter Physics in the Modeling of mJ-Class Laser-Induced Surface AblationUltrafast laser heating of electrons on a metal surface breaks the pressure equilibrium within the material, thus initiating ablation. The stasis of a room-temperature metal results from a balance…Asher Davidson, George Petrov, Daniel Gordon et al.·Jun 24, 2021SaveLearn
On the Application of the Analytical Discrete Ordinates Method to the Solution of Nonclassical Transport Problems in Slab GeometryIn this work we investigate the use of the Analytical Discrete Ordinates (ADO) method when solving the spectral approximation of the nonclassical transport equation. The spectral approximation is a…Leonardo R. C. Moraes, Liliane B. Barichello, Ricardo C. Barros et al.·Jun 24, 2021SaveLearn
Simulating both parity sectors of the Hubbard Model with Tensor NetworksTensor networks are a powerful tool to simulate a variety of different physical models, including those that suffer from the sign problem in Monte Carlo simulations. The Hubbard model on the…Manuel Schneider, Johann Ostmeyer, Karl Jansen et al.·Jun 24, 2021SaveLearn
Lettuce: PyTorch-based Lattice Boltzmann FrameworkThe lattice Boltzmann method (LBM) is an efficient simulation technique for computational fluid mechanics and beyond. It is based on a simple stream-and-collide algorithm on Cartesian grids, which is…Mario Christopher Bedrunka, Dominik Wilde, Martin Kliemank et al.·Jun 24, 2021SaveLearn
Design and engineering of a simplified workflow execution for the MG5aMC event generator on GPUs and vector CPUsPhysics event generators are essential components of the data analysis software chain of high energy physics experiments, and important consumers of their CPU resources. Improving the software…Andrea Valassi, Stefan Roiser, Olivier Mattelaer et al.·Jun 23, 2021SaveLearn
Machine learning structure preserving brackets for forecasting irreversible processesForecasting of time-series data requires imposition of inductive biases to obtain predictive extrapolation, and recent works have imposed Hamiltonian/Lagrangian form to preserve structure for systems…Kookjin Lee, Nathaniel A. Trask, Panos Stinis·Jun 23, 2021SaveLearn
A Hybrid Nodal-Staggered Pseudo-Spectral Electromagnetic Particle-In-Cell Method with Finite-Order CenteringElectromagnetic particle-in-cell (PIC) codes are widely used to perform computer simulations of a variety of physical systems, including fusion plasmas, astrophysical plasmas, plasma wakefield…Edoardo Zoni, Remi Lehe, Olga Shapoval et al.·Jun 23, 2021SaveLearn
A finite difference scheme for integrating the Takagi-Taupin equations on an arbitrary orthogonal gridCalculating dynamical diffraction patterns for X-ray topography and similar x-ray scattering-imaging techniques require the numerical integration of the Takagi-Taupin equations. This is usually…Mads Carlsen, Hugh Simons·Jun 23, 2021SaveLearn
Charged Oxygen Vacancy Induced Ferroelectric Structure Transition in Hafnium OxideThe discovery of ferroelectric HfO2 in thin films and more recently in bulk is an important breakthrough because of its silicon-compatibility and unexpectedly persistent polarization at low…Ri He, Hongyu Wu, Shi Liu et al.·Jun 23, 2021SaveLearn
Electronic structure of water from Koopmans-compliant functionalsObtaining a precise theoretical description of the spectral properties of liquid water poses challenges for both molecular dynamics (MD) and electronic structure methods. The lower computational cost…James Moraes de Almeida, Ngoc Linh Nguyen, Nicola Colonna et al.·Jun 22, 2021SaveLearn
GPU-accelerated Monte Carlo simulations of anisotropic Heisenberg ferromagnetsThe Monte Carlo method is a powerful technique for computing thermodynamic magnetic states of otherwise unsolvable spin Hamiltonians, but the method becomes computationally prohibitive with…Michalis Charilaou·Jun 21, 2021SaveLearn
Direct N-Body problem optimisation using the AVX-512 instruction setThe integration of the equations of motion of N interacting particles, represents a classical problem in many branches of physics and chemistry. The direct N-body problem is at the heart of…Jofre Pedregosa-Gutierrez, Jim Dempsey·Jun 21, 2021SaveLearn
First principle investigations of the structural, electronic, and phase stability in 2D layered ZnSbRecently, the two dimensional (2D) materials have become a potential candidates for various technological applications in spintronics and optoelectronics. In the present study, the structural,…Dinesh Thapa, Junseong Song, Vivek Dixit et al.·Jun 20, 2021SaveLearn
MadFlow: automating Monte Carlo simulation on GPU for particle physics processesWe present MadFlow, a first general multi-purpose framework for Monte Carlo (MC) event simulation of particle physics processes designed to take full advantage of hardware accelerators, in…Stefano Carrazza, Juan Cruz-Martinez, Marco Rossi et al.·Jun 18, 2021SaveLearn
The Breaking of Geometric Constraint of Classical Dimers on the Square LatticeWe study a model of two-dimensional classical dimers on the square lattice with strong geometric constraints (there is exactly one bond with the nearest point for every point in the lattice). This…Hongxu Yao, Jiaze Li, Jintao Hou·Jun 17, 2021SaveLearn
Targeted free energy perturbation revisited: Accurate free energies from mapped reference potentialsWe present an approach that extends the theory of targeted free energy perturbation (TFEP) to calculate free energy differences and free energy surfaces at an accurate quantum mechanical level of…Andrea Rizzi, Paolo Carloni, Michele Parrinello·Jun 17, 2021SaveLearn
Covariance-based smoothed particle hydrodynamics. A machine-learning application to simulating disc fragmentationA PCA-based, machine learning version of the SPH method is proposed. In the present scheme, the smoothing tensor is computed to have their eigenvalues proportional to the covariance's principal…Eraldo Pereira Marinho·Jun 16, 2021SaveLearn
Deep neural networks for high harmonic spectroscopy in solidsNeural networks are a prominent tool for identifying and modeling complex patterns, which are otherwise hard to detect and analyze. While machine learning and neural networks have been finding…Nikolai D. Klimkin, Álvaro Jiménez-Galán, Rui E. F. Silva et al.·Jun 16, 2021SaveLearn