Physics-embedded inverse analysis with automatic differentiation for the earth's subsurfaceInverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse…Hao Wu, Sarah Greer, Daniel O'Malley·Aug 8, 2022SaveLearn
Characterization of Transmission Lines in Microelectronics Circuits using the ARTEMIS SolverModeling and characterization of electromagnetic wave interactions with microelectronic devices to derive network parameters has been a widely used practice in the electronic industry. However, as…Saurabh S. Sawant, Zhi Yao, Revathi Jambunathan et al.·Aug 8, 2022SaveLearn
Anisotropic and high thermal conductivity in monolayer quasi-hexagonal fullerene: A comparative study against bulk phase fullereneRecently a novel two-dimensional (2D) C60 based crystal called quasi-hexagonal-phase fullerene (QHPF) has been fabricated and demonstrated to be a promising candidate for 2D electronic devices…Haikuan Dong, Chenyang Cao, Penghua Ying et al.·Aug 8, 2022SaveLearn
Preconditioned Central Moment Lattice Boltzmann Method on a Rectangular Lattice Grid for Accelerated Computations of Inhomogeneous FlowsConvergence acceleration of flow simulations to their steady states at lower Mach numbers can be achieved via preconditioning the lattice Boltzmann (LB) schemes that alleviate the associated…Eman Yahia, Kannan Premnath·Aug 6, 2022SaveLearn
EURADOS Working Group 6, Computational Dosimetry, a history of promoting good practice via intercomparisons and trainingThis paper is the editorial of a special issue of Radiation Measurements on EURADOS intercomparisons in computational dosimetry. The articles in this special issue cover complex problems in terms of…Rick Tanner, Stefano Agosteo, Hans Rabus·Aug 4, 2022SaveLearn
Estimating relative diffusion from 3D micro-CT images using CNNsIn the past several years, convolutional neural networks (CNNs) have proven their capability to predict characteristic quantities in porous media research directly from pore-space geometries. Due to…Stephan Gärttner, Florian Frank, Fabian Woller et al.·Aug 4, 2022SaveLearn
Analytical formulas for calculating the thermal diffusivity of cylindrical shell and spherical shell samplesCalculating the thermal diffusivity of solid materials is commonly carried out using the laser flash experiment. This classical experiment considers a small (usually thin disc-shaped) sample of the…Elliot J. Carr, Luke P. Filippini·Aug 3, 2022SaveLearn
Predicting rare events using neural networks and short-trajectory dataEstimating the likelihood, timing, and nature of events is a major goal of modeling stochastic dynamical systems. When the event is rare in comparison with the timescales of simulation and/or…John Strahan, Justin Finkel, Aaron R. Dinner et al.·Aug 2, 2022SaveLearn
A physics-defined recurrent neural network to compute coherent light wave scattering on the millimetre scaleHeterogeneous materials such as biological tissue scatter light in random, yet deterministic, ways. Wavefront shaping can reverse the effects of scattering to enable deep-tissue microscopy. Such…Laurynas Valantinas, Tom Vettenburg·Aug 1, 2022SaveLearn
Pipeline for Automating Compliance-based Elimination and Extension (PACE2): A Systematic Framework for High-throughput Biomolecular Material Simulation WorkflowsThe formation of biomolecular materials via dynamical interfacial processes such as self-assembly and fusion, for diverse compositions and external conditions, can be efficiently probed using…Srinivas C. Mushnoori, Ethan Zang, Akash Banerjee et al.·Jul 29, 2022SaveLearn
Fast, hierarchical, and adaptive algorithm for Metropolis Monte Carlo simulations of long-range interacting systemsWe present a fast, hierarchical, and adaptive algorithm for Metropolis Monte Carlo simulations of systems with long-range interactions that reproduces the dynamics of a standard implementation…Fabio Müller, Henrik Christiansen, Stefan Schnabel et al.·Jul 29, 2022SaveLearn
Conditioning Normalizing Flows for Rare Event SamplingUnderstanding the dynamics of complex molecular processes is often linked to the study of infrequent transitions between long-lived stable states. The standard approach to the sampling of such rare…Sebastian Falkner, Alessandro Coretti, Salvatore Romano et al.·Jul 29, 2022SaveLearn
Untrained physically informed neural network for image reconstruction of magnetic field sourcesPredicting measurement outcomes from an underlying structure often follows directly from fundamental physical principles. However, a fundamental challenge is posed when trying to solve the inverse…A. E. E. Dubois, D. A. Broadway, A. Stark et al.·Jul 27, 2022SaveLearn
Arbitrary unitary rotation of three-dimensional pixellated imagesUsing the coefficients introduced by Bargmann and Moshinsky for the reduction of the su(3) algebra of Cartesian three-dimensional oscillator multiplet states into so(3) angular momentum…Alejandro R. Urzúa, Kurt Bernardo Wolf·Jul 27, 2022SaveLearn
A Riemannian Stochastic Representation for Quantifying Model Uncertainties in Molecular Dynamics SimulationsA Riemannian stochastic representation of model uncertainties in molecular dynamics is proposed. The approach relies on a reduced-order model, the projection basis of which is randomized on a subset…Hao Zhang, Johann Guilleminot·Jul 26, 2022SaveLearn
MD-Bench: A generic proxy-app toolbox for state-of-the-art molecular dynamics algorithmsProxy-apps, or mini-apps, are simple self-contained benchmark codes with performance-relevant kernels extracted from real applications. Initially used to facilitate software-hardware co-design, they…Rafael Ravedutti Lucio Machado, Jan Eitzinger, Harald Köstler et al.·Jul 26, 2022SaveLearn
Physics Embedded Machine Learning for Electromagnetic Data ImagingElectromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally…Rui Guo, Tianyao Huang, Maokun Li et al.·Jul 26, 2022SaveLearn
A divergence-free constrained magnetic field interpolation method for scattered dataAn interpolation method to evaluate magnetic fields given unstructured, scattered magnetic data is presented. The method is based on the reconstruction of the global magnetic field using a…Minglei Yang, Diego del-Castillo-Negrete, Guannan Zhang et al.·Jul 25, 2022SaveLearn
Double excitation energies from quantum Monte Carlo using state-specific energy optimizationWe show that recently developed quantum Monte Carlo methods, which provide accurate vertical transition energies for single excitations, also successfully treat double excitations. We study the…Stuart Shepard, Ramón Lorenzo Panadés-Barrueta, Saverio Moroni et al.·Jul 25, 2022SaveLearn
Spin-transfer and spin-orbit torques in the Landau-Lifshitz-Gilbert equationDynamic simulations of spin-transfer and spin-orbit torques are increasingly important for a wide range of spintronic devices including magnetic random access memory, spin-torque nano-oscillators and…Andrea Meo, Carenza E. Cronshaw, Sarah Jenkins et al.·Jul 25, 2022SaveLearn
Initial Orbit Determination for the CR3BP using Particle Swarm OptimizationThis work utilizes a particle swarm optimizer (PSO) for initial orbit determination for a chief and deputy scenario in the circular restricted three-body problem (CR3BP). The PSO is used to minimize…David Zuehlke, Taylor Yow, Daniel Posada et al.·Jul 23, 2022SaveLearn
Fast, feature-rich weakly-compressible SPH on GPU: coding strategies and compiler choicesGPUSPH was the first implementation of the weakly-compressible Smoothed Particle Hydrodynamics method to run entirely on GPU using CUDA. Version 5, released in June 2018, features a radical…Giuseppe Bilotta, Vito Zago, Alexis Hérault et al.·Jul 22, 2022SaveLearn
Simulation of Noncircular Rigid Bodies: Machine Learning Based Overlap Calculation Technique with System Size Independent Computational CostStandard molecular dynamics (MD) and Monte Carlo (MC) simulation deals with spherical particles. Extending these standard simulation methodologies to the non-spherical cases is non-trivial. To…Saientan Bag·Jul 22, 2022SaveLearn
Deep Learning of Radiative Atmospheric Transfer with an AutoencoderAs electro-optical energy from the sun propagates through the atmosphere it is affected by radiative transfer effects including absorption, emission, and scattering. Modeling these affects is…Abigail Basener, Bill Basener·Jul 21, 2022SaveLearn
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networksPhysics-informed neural networks (PINNs) have shown to be an effective tool for solving forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs into the loss of…Chenxi Wu, Min Zhu, Qinyang Tan et al.·Jul 21, 2022SaveLearn