UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter CalibrationThe goal of this paper is to demonstrate and address challenges related to all aspects of performing a complete uncertainty quantification analysis of a complicated physics-based simulation like a 2D…Georgios Georgalis, Alejandro Becerra, Kenneth Budzinski et al.·Nov 9, 2024SaveLearn
Can Efficient Fourier-Transform Techniques Favorably Impact on Broadband Computational Electromagnetism?In view of recently demonstrated joint use of novel Fourier-transform techniques and effective high-accuracy frequency domain solvers related to the Method of Moments, it is argued that a set of…Thomas G. Anderson, Mark Lyon, Tao Yin et al.·Nov 8, 2024SaveLearn
Many body theory beyond GW : towards a complete description of 2-body correlated propagationStarting with Hedins equations, simple expressions for the irreducible self-energy are derived. The derivation with vertex effects included in the self-energy results in a number of terms beyond GW…Brian Cunningham·Nov 6, 2024SaveLearn
An iterative scheme to include turbulent diffusion in advective-dominated transport of delayed neutron precursorsIn this study, the Method of Characteristics (MOC) for Delayed Neutron Precursors (DNPs) is used to solve the precursors balance equation with turbulent diffusion. The diffusivity of DNPs,…Mathis Caprais, André Bergeron·Nov 6, 2024SaveLearn
Neurons for Neutrons: A Transformer Model for Computation Load Estimation on Domain-Decomposed Neutron Transport ProblemsDomain decomposition is a technique used to reduce memory overhead on large neutron transport problems. Currently, the optimal load-balanced processor allocation for these domains is typically…Alexander Mote, Todd Palmer, Lizhong Chen·Nov 5, 2024SaveLearn
Ka-chow! A simple irregular 2D lattice model of lightningA model of lightning that captures the propagation of lightning channels on an irregular lattice is developed. The irregular lattice allows us to capture large two-dimensional systems (2 km x 2 km),…Gavin Buxton·Nov 5, 2024SaveLearn
Adaptive-precision potentials for large-scale atomistic simulationsLarge-scale atomistic simulations rely on interatomic potentials providing an efficient representation of atomic energies and forces. Modern machine-learning (ML) potentials provide the most precise…David Immel, Ralf Drautz, Godehard Sutmann·Nov 5, 2024SaveLearn
KinetiX: A performance portable code generator for chemical kinetics and transport propertiesWe present KinetiX, a software toolkit to generate computationally efficient fuel-specific routines for the chemical source term, thermodynamic and mixture-averaged transport properties for use in…Bogdan A. Danciu, Christos E. Frouzakis·Nov 4, 2024SaveLearn
Computing critical exponents in 3D Ising model via pattern recognition/deep learning approachIn this study, we computed three critical exponents (α, β, γ) for the 3D Ising model with Metropolis Algorithm using Finite-Size Scaling Analysis on six cube length scales…Timothy A. Burt·Nov 4, 2024SaveLearn
Neural network-based nodal structures optimization for interacting fermionic systemsSimulating strongly correlated fermionic systems remains a fundamental challenge in quantum physics, largely due to the sign problem in quantum Monte Carlo (QMC) methods. We present a neural…William Freitas, B. Abreu, S. A. Vitiello·Nov 4, 2024SaveLearn
Time-multiplexed Reservoir Computing with Quantum-Dot Lasers: Does more complexity lead to better performance?Reservoir computing with optical devices offers an energy-efficient approach for time-series forecasting. Quantum dot lasers with feedback are modelled in this paper to explore the extent to which…Huifang Dong, Lina Jaurigue, Kathy Lüdge·Nov 4, 2024SaveLearn
Semi-implicit Lax-Wendroff kinetic scheme for multi-scale phonon transportFast and accurate predictions of the spatiotemporal distributions of temperature are crucial to the multi-scale thermal management and safe operation of microelectronic devices. To realize it, an…Shuang Peng, Songze Chen, Hong Liang et al.·Nov 4, 2024SaveLearn
Physics-Constrained Graph Neural Networks for Spatio-Temporal Prediction of Drop Impact on OLED Display PanelsThis study aims to predict the spatio-temporal evolution of physical quantities observed in multi-layered display panels subjected to the drop impact of a ball. To model these complex interactions,…Jiyong Kim, Jangseop Park, Nayong Kim et al.·Nov 4, 2024SaveLearn
Variance-reduced random batch Langevin dynamicsThe random batch method is advantageous in accelerating force calculations in particle simulations, but it poses a challenge of removing the artificial heating effect in application to the Langevin…Zhenli Xu, Yue Zhao, Qi Zhou·Nov 4, 2024SaveLearn
Petrov-Galerkin model reduction for thermochemical nonequilibrium gas mixturesState-specific thermochemical collisional models are crucial to accurately describe the physics of systems involving nonequilibrium plasmas, but they are also computationally expensive and…Ivan Zanardi, Alberto Padovan, Daniel J. Bodony et al.·Nov 3, 2024SaveLearn
Numerical Modeling of Liquid Wall Flows for Fusion Energy Applications Using Maxwell-Navier-Stokes EquationsDuring the Z-Pinch fusion process, electric current is injected into liquid metal from the plasma column, generating Lorentz forces that deform the liquid metal's free surface. Modeling this…Suresh Murugaiyan, Stefano Brizzolara·Nov 2, 2024SaveLearn
Extreme Value Statistics of Community Detection in Complex Networks with Reduced Network Extremal Ensemble Learning (RenEEL)Arguably, the most fundamental problem in Network Science is finding structure within a complex network. One approach is to partition the nodes into communities that are more densely connected than…Tania Ghosh, R. K. P. Zia, Kevin E. Bassler·Nov 1, 2024SaveLearn
Ensemble Monte Carlo Calculations with Five Novel MovesWe introduce five novel types of Monte Carlo (MC) moves that brings the number of moves of ensemble MC calculations from three to eight. So far such calculations have relied on affine invariant…Burkhard Militzer·Nov 1, 2024SaveLearn
Machine learning models for Si nanoparticle growth in nonthermal plasmaNanoparticles (NPs) formed in nonthermal plasmas (NTPs) can have unique properties and applications. However, modeling their growth in these environments presents significant challenges due to the…Matt Raymond, Paolo Elvati, Jacob C. Saldinger et al.·Oct 31, 2024SaveLearn
Volumetric lattice Boltzmann method for thermal particulate flows with conjugate heat transferA volumetric lattice Boltzmann (LB) method is developed for the particle-resolved direct numerical simulation of thermal particulate flows with conjugate heat transfer. This method is devised as a…Xiaojie Zhang, Donglei Wang, Qing Li et al.·Oct 31, 2024SaveLearn
Machine learning models with different cheminformatics data sets to forecast the power conversion efficiency of organic solar cellsRandom Forest (RF) and Gradient Boosting Regression Trees (GBRT) regression models along with three cheminformatics data sets (RDkit, Mordred, Morgan) have been used to predict the power conversion…Omar A. Alvarez-Gonzaga, Ulises A. Vergara-Beltran, Juan I. Rodriguez·Oct 30, 2024SaveLearn
SLICES-PLUS: A Crystal Representation Leveraging Spatial SymmetryIn recent years, the realm of crystalline materials has witnessed a surge in the development of generative models, predominantly aimed at the inverse design of crystals with tailored physical…Baoning Wang, Zhiyuan Xu, Zhiyu Han et al.·Oct 30, 2024SaveLearn
From Mesh to Neural Nets: A Multi-Method Evaluation of Physics-Informed Neural Networks and Galerkin Finite Element Method for Solving Nonlinear Convection-Reaction-Diffusion EquationsNon-linear convection-reaction-diffusion (CRD) partial differential equations (PDEs) are crucial for modeling complex phenomena in fields such as biology, ecology, population dynamics, physics, and…Fardous Hasan, Hazrat Ali, Hasan Asyari Arief·Oct 29, 2024SaveLearn
The Performance of MC X-ray and PENELOPE in Homogeneous Bulk SamplesThis manuscript presents a comparative analysis of two software packages, MC X-ray and PENELOPE, focusing on their accuracy and efficiency in simulating k-ratios for binary compounds and comparing…Dawei Gao, Yu Yuan, Nicolas Brodusch et al.·Oct 29, 2024SaveLearn
A Message Passing Neural Network Surrogate Model for Bond-Associated Peridynamic Material Correspondence FormulationPeridynamics is a non-local continuum mechanics theory that offers unique advantages for modeling problems involving discontinuities and complex deformations. Within the peridynamic framework,…Xuan Hu, Qijun Chen, Nicholas H. Luo et al.·Oct 29, 2024SaveLearn