Complexity order of multiple resource algorithmsAlgorithmic efficiency is essential to reducing energy and time usage for computational problems. Optimizing efficiency is important for tasks involving multiple resources, for example in stochastic…Run Yan Teh, Manushan Thenabadu, Peter D Drummond·Jul 8, 2025SaveLearn
Reduced SIGMA Basis Sets: a new family of SIGMA basis sets for molecular calculationsA new family of Gaussian-type SIGMA basis sets, termed reduced SIGMA basis sets, is introduced and preliminarily tested. Sharing the same composition as Dunning basis sets, they enhance performance…Ignacio Ema, Jesús San-Fabián, Guillermo Ramírez et al.·Jul 7, 2025SaveLearn
RePlaChem: A dimensionality reduction library for plasma chemical mechanismsIn this work, we present RePlaChem, a software library for reducing detailed large-scale plasma chemical mechanisms to smaller skeletal ones. The library parses a plasma chemical mechanism in the…Z. Nikolaou, E. Morais, S. Van Rompaey et al.·Jul 6, 2025SaveLearn
High-Fidelity Modelling of the Molten Salt Fast ReactorThe Molten Salt Fast Reactor (MSFR) is one of the six GEN-IV reactor designs. In the MSFR, the liquid fuel is the coolant, which moves throughout the primary circuit. This complex phenomenology…Maximiliano Dalinger, Elia Merzari, Saya Lee et al.·Jul 5, 2025SaveLearn
General synthetic iterative scheme for multiscale radiative transfer in the finite-volume frameworkAchieving efficient and accurate simulation of the radiative transfer has long been a research challenge. Here we introduce the general synthetic iterative scheme as an easy-to-implement approach to…Kaiyuan Wang, Yanbing Zhang, Qi Li et al.·Jul 5, 2025SaveLearn
Sequential Neural Operator Transformer for High-Fidelity Surrogates of Time-Dependent Non-linear Partial Differential EquationsPartial differential equations (PDEs) are fundamental to modeling complex and nonlinear physical phenomena, but their numerical solution often requires significant computational resources,…Qibang Liu, Seid Koric·Jul 4, 2025SaveLearn
Fast prediction of plasma instabilities with sparse-grid-accelerated optimized dynamic mode decompositionParametric data-driven reduced-order models (ROMs) that embed dependencies in a large number of input parameters are crucial for enabling many-query tasks in large-scale problems. These tasks,…Kevin Gill, Ionut-Gabriel Farcas, Silke Glas et al.·Jul 4, 2025SaveLearn
Estimating Free Parameters in Stochastic Oscillatory Models Using a Weighted Cost FunctionIn this study, we estimate parameters in stochastic oscillatory systems by developing a novel cost function. This function incorporates power spectral density, analytic signal, and position…Joseph M. Marcinik, Dzmitry Vaido, Dolores Bozovic·Jul 3, 2025SaveLearn
A Multi-Level Monte Carlo Tree Search Method for Configuration Generation in Crystalline SystemsIn this paper, we study the construction of structural models for the description of substitutional defects in crystalline materials. Predicting and designing the atomic structures in such systems is…Xiaoxu Li, Ge Xu, Huajie Chen et al.·Jul 3, 2025SaveLearn
An Algorithm for Automated Extraction of Resonance Parameters from the Stabilization MethodThe application of the stabilization method [A.~U.\ Hazi and H.~S.\ Taylor, Phys.~Rev.~A 1, 1109 (1970)]) to extract accurate energy and lifetimes of resonance states is challenging: The…Johanna Langner, Anjan Sadhukhan, Jayanta K. Saha et al.·Jul 2, 2025SaveLearn
An improved argument principle root-search method for modes of slab waveguides, optical fibers, and spheresWe update our root-search method for transcendental equations. Our method is globally convergent and is guaranteed to locate all complex roots within a specified search domain, since it is based on…S. Rao, P. Y. Chen, T. Grossinger et al.·Jul 1, 2025SaveLearn
Nine circles of elastic brittle fracture: A series of challenge problems to assess fracture modelsSince the turn of the millennium, capitalizing on modern advances in mathematics and computation, a slew of computational models have been proposed in the literature with the objective of describing…Farhad Kamarei, Bo Zheng, John E. Dolbow et al.·Jun 30, 2025SaveLearn
Modified non-local damage model: resolving spurious damage evolutionAccurate prediction of damage and fracture evolution is critical for the safety design and preventive maintenance of engineering structures, however existing computational methods face significant…Roshan Philip Saji, Panos Pantidis, Mostafa E. Mobasher·Jun 30, 2025SaveLearn
A hybrid numerical algorithm based on the stochastic particle Shakhov and DSMC methodThe Direct Simulation Monte Carlo (DSMC) method is widely employed for simulating rarefied nonequilibrium gas flows. With advances in aerospace engineering and micro/nano-scale technologies, gas…Hao Jin, Sha Liu, Sirui Yang et al.·Jun 30, 2025SaveLearn
Learning robust parameter inference and density reconstruction in flyer plate impact experimentsEstimating physical parameters or material properties from experimental observations is a common objective in many areas of physics and material science. In many experiments, especially in shock…Evan Bell, Daniel A. Serino, Ben S. Southworth et al.·Jun 30, 2025SaveLearn
Efficient snap-to-contact computations for van der Waals interacting fibersWe consider van der Waals interactions between in-plane fibers, where the computational model employs the Lennard-Jones potential and the coarse-grained approach. The involved 6D integral over two…Aleksandar Borković, Michael H. Gfrerer, Roger A. Sauer et al.·Jun 30, 2025SaveLearn
Graph Neural Networks to Predict Coercivity of Hard Magnetic MicrostructuresGraph neural networks (GNN) are a promising tool to predict magnetic properties of large multi-grain structures, which can speed up the search for rare-earth free permanent magnets. In this paper, we…Heisam Moustafa, Alexander Kovacs, Johann Fischbacher et al.·Jun 30, 2025SaveLearn
Regularized Fluctuating Lattice Boltzmann ModelWe introduce a regularized fluctuating lattice Boltzmann model (Reg-FLBM) for the D3Q27 lattice, which incorporates thermal fluctuations through Hermite-based projections to ensure compliance with…Marco Lauricella, Andrea Montessori, Adriano Tiribocchi et al.·Jun 29, 2025SaveLearn
Variational PINNs with tree-based integration and boundary element data in the modeling of multi-phase architected materialsThe current contribution develops a Variational Physics-Informed Neural Network (VPINN)-based framework for the analysis and design of multiphase architected solids. The elaborated VPINN methodology…Dimitrios C. Rodopoulos, Panos Pantidis, Nikolaos Karathanasopoulos·Jun 29, 2025SaveLearn
Generating Moving Field Initial Conditions with Spatially Varying BoostWe introduce a novel class of algorithms, the ``spatially varying boost'', for generating dynamical field initial conditions with prescribed bulk velocities. Given (non-moving) initial field data,…Siyang Ling·Jun 28, 2025SaveLearn
AirCANS: CFD 2D Mesh Optimisation-based Airfoil Classification and Assessment using Neural NetworksThis study explores the possibilities of automating the loading, classification and assessment of Computational Fluid Dynamics (CFD) mesh data by Convolutional Neural Networks (CNNs). The research…Lushun Fan, Yuqin Xia, Jun Li et al.·Jun 27, 2025SaveLearn
Extending OpenMC Validation to Spent Fuel Canisters: A Criticality Benchmark Against MCNPOpenMC is an open-source Monte Carlo code with increasing relevance in criticality safety and reactor physics applications. While its validation has covered a broad range of systems, its performance…Javier Ruiz-Pineda, Jaime Romero-Barrientos, Francisco Molina et al.·Jun 27, 2025SaveLearn
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and ApplicationsUniversal machine-learned interatomic potentials (U-MLIPs) have demonstrated broad applicability across diverse atomistic systems but often require fine-tuning to achieve task-specific accuracy.…Xiaoqing Liu, Kehan Zeng, Zedong Luo et al.·Jun 27, 2025SaveLearn
Benchmarking and Parallelization of Electrostatic Particle-In-Cell for low-temperature Plasma Simulation by particle-thread BindingThe Particle-In-Cell (PIC) method for plasma simulation tracks particle phase space information using particle and grid data structures. High computational costs in 2D and 3D device-scale PIC…Libn Varghese, Bhaskar Chaudhury, Miral Shah et al.·Jun 26, 2025SaveLearn
Anharmonic phonons via quantum thermal bath simulationsLattice vibrations within crystalline solids, or phonons, provide information on a variety of important material characteristics, from thermal qualities to optical properties and phase transition…Taylor Baird, Rodolphe Vuilleumier, Sara Bonella·Jun 26, 2025SaveLearn