Petrov-Galerkin model reduction for collisional-radiative argon plasmaHigh-fidelity simulation of nonequilibrium plasmas -- crucial to applications in electric propulsion, hypersonic re-entry, and astrophysical flows -- requires state-specific collisional-radiative…Ivan Zanardi, Alessandro Meini, Alberto Padovan et al.·Jun 5, 2025SaveLearn
Reduction of Outflow Boundary Influence on Aerodynamic Performance using Neural NetworksThe accurate treatment of outflow boundary conditions remains a critical challenge in computational fluid dynamics when predicting aerodynamic forces and/or acoustic emissions. This is particularly…Mario Christopher Bedrunka, Dirk Reith, Holger Foysi et al.·Jun 5, 2025SaveLearn
Thermoplasmonics of Gold-Core Silica-Shell Colloidal Nanoparticles under Pulse IlluminationCore-shell nanoparticles, particularly those having a gold core, have emerged as a highly promising class of materials due to their unique optical and thermal properties, which underpin a wide range…Julien El Hajj, Gilles Ledoux, Samy Merabia·Jun 5, 2025SaveLearn
A highly scalable numerical framework for reservoir simulation on UG4 platformThe modeling and simulation of multiphase fluid flow receive significant attention in reservoir engineering. Many time discretization schemes for multiphase flow equations are either explicit or…Shuai Lu·Jun 5, 2025SaveLearn
Near-field-free super-potential FFT method for the three-dimensional free-space Poisson equationWe present a spectrally accurate, efficient FFT-based method for the three-dimensional free-space Poisson equation with smooth, compactly supported sources. The method adopts a super-potential…Lukas Exl, Sebastian Schaffer·Jun 4, 2025SaveLearn
BridgeNet: A Hybrid, Physics-Informed Machine Learning Framework for Solving High-Dimensional Fokker-Planck EquationsBridgeNet is a novel hybrid framework that integrates convolutional neural networks with physics-informed neural networks to efficiently solve non-linear, high-dimensional Fokker-Planck equations…Elmira Mirzabeigi, Rezvan Salehi, Kourosh Parand·Jun 4, 2025SaveLearn
chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulationsMachine learning potentials (MLPs) have advanced rapidly and show great promise to transform molecular dynamics (MD) simulations. However, most existing software tools are tied to specific MLP…Paul Fuchs, Weilong Chen, Stephan Thaler et al.·Jun 4, 2025SaveLearn
Efficient implementation of the quasiparticle self-consistent GW method on GPUWe have developed a multi-GPU version of the quasiparticle self-consistent GW (QSGW), a cutting-edge method for describing electronic excitations in a first-principles approach. While the QSGW…Masao Obata, Takao Kotani, Tatsuki Oda·Jun 4, 2025SaveLearn
GP-Recipe: Gaussian Process approximation to linear operations in numerical methodsWe introduce new Gaussian Process (GP) high-order approximations to linear operations that are frequently used in various numerical methods. Our method employs the kernel-based GP regression…Christopher DeGrendele, Dongwook Lee·Jun 4, 2025SaveLearn
Superatomic hydrogen: achieving effective aggregation of hydrogen atoms at pressures lower than that of metallic hydrogenMetal hydrogen exhibiting electron delocalization properties has been recognized as an important prospect for achieving controlled nuclear fusion, but the extreme pressure conditions required…Jia Fan, Chenxi Wan, Rui Liu et al.·Jun 4, 2025SaveLearn
A three-dimensional energy flux acoustic propagation modelThis paper extends energy flux methods to handle three-dimensional ocean acoustic environments, the implemented solution captures horizontally refracted incoherent acoustic intensity, and its…Mark Langhirt, Charles Holland, Ying-Tsong Lin·Jun 3, 2025SaveLearn
Accuracy and scalability of asynchronous compressible flow solver for transitional flowsTo overcome the communication bottlenecks observed in state-of-the-art parallel time-dependent flow solvers at extreme scales, an asynchronous computing approach that relaxes communication and…Aswin Kumar Arumugam, Shubham Kumar Goswami, Nagabhushana Rao Vadlamani et al.·Jun 3, 2025SaveLearn
An open-source finite element toolbox for anisotropic creep and irradiation growth: Application to tube and spacer grid assemblyThis work presents an open-source interface that couples the viscoplastic self-consistent (VPSC) model capable of simulating anisotropic creep and irradiation growth in polycrystalline materials with…Fabrizio E. Aguzzi, Santiago M. Rabazzi, Martín S. Armoa et al.·Jun 3, 2025SaveLearn
An efficient GPU-accelerated adaptive mesh refinement framework for high-fidelity compressible reactive flows modelingThis paper presents a heterogeneous adaptive mesh refinement (AMR) framework for efficient simulation of moderately stiff reactive problems. This framework features an elaborate subcycling-in-time…Yuqi Wang, Yadong Zeng, Ralf Deiterding et al.·Jun 3, 2025SaveLearn
Modeling and Simulation of Coupled Biochemical and Two-Phase Compositional Flow in Underground Hydrogen StorageIntegrating microbial activity into underground hydrogen storage models is crucial for simulating long-term reservoir behavior. In this work, we present a coupled framework that incorporates…Elyes Ahmed, Brahim Amaziane, Salaheddine Chabab et al.·Jun 3, 2025SaveLearn
The transformative capability of quantum-accurate machine learning interatomic potentialsMany materials's properties and phase boundaries are generally not well known under extreme pressure and temperature conditions. This is a consequence of the scarcity of experimental information and…Alfredo A. Correa, Sebastien Hamel·Jun 2, 2025SaveLearn
A Concurrent Multiscale Framework Coupling Direct Simulation Monte Carlo and Molecular DynamicsWe present a new method to couple the Direct Simulation Monte Carlo (DSMC) algorithm with molecular dynamics (MD). The coupling approach generalizes prior coupling methods using a cell-based…Tim Linke, Dane Sterbentz, Niels Grønbech-Jensen et al.·Jun 2, 2025SaveLearn
Modeling the Optical Properties of Biological Structures using Symbolic RegressionWe present a Machine Learning approach based on Symbolic Regression to derive, from either numerically generated or experimentally measured spectral data, closed-form expressions that model the…Julian Sierra-Velez, Alexandre Vial, Marina Inchaussandague et al.·Jun 2, 2025SaveLearn
Benchmarking Universal Machine Learning Interatomic Potentials for Real-Time Analysis of Inelastic Neutron Scattering DataThe accurate calculation of phonons and vibrational spectra remains a significant challenge, requiring highly precise evaluations of interatomic forces. Traditional methods based on the quantum…Bowen Han, Yongqiang Cheng·Jun 2, 2025SaveLearn
A Graph Neural Network for the Era of Large Atomistic ModelsFoundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functional theory (DFT).…Duo Zhang, Anyang Peng, Chun Cai et al.·Jun 2, 2025SaveLearn
Lattice Boltzmann Boundary Conditions for Flow, Convection-Diffusion and MHD SimulationsA general derivation is proposed for several boundary conditions arisen in the lattice Boltzmann simulations of various physical problems. Pair-wise moment conservations are proposed to enforce the…Jun Li, Wai Hong Ronald Chan, Zhe Feng et al.·Jun 1, 2025SaveLearn
Higher-Order Automatic Differentiation Using Symbolic Differential Algebra: Bridging the Gap between Algorithmic and Symbolic DifferentiationIn scientific computation, it is often necessary to calculate higher-order derivatives of a function. Currently, two primary methods for higher-order automatic differentiation exist: symbolic…He Zhang·Jun 1, 2025SaveLearn
Antenna Q-Factor Topology Optimization with Auxiliary Edge ResistivitiesThis paper presents a novel bi-level topology optimization strategy within the method-of-moments paradigm. The proposed approach utilizes an auxiliary variables called edge resistivities related to…Stepan Bosak, Miloslav Capek, Jiri Matas·May 31, 2025SaveLearn
Comparing the Performance of MC/DC's on-GPU Event-based Processing Methods in Multigroup and Continuous-energy ProblemsMonte Carlo / Dynamic Code (MC/DC) is a portable Monte Carlo neutron transport package for rapid numerical methods exploration in heterogeneous and HPC contexts, developed under the auspices of the…Braxton Cuneo, Joanna Piper Morgan, Ilham Variansyah et al.·May 30, 2025SaveLearn
Potential Effects of Loading Terminal Locations on Surface Trajectories of Oil Spill TransportWe present an investigation comparing the potential impacts of offshore and onshore crude oil loading sites on surface trajectories of spilled oil particles in the regions near the Port of Corpus…Shoshana Reich, Edward Buskey, Clint Dawson et al.·May 30, 2025SaveLearn