Atomic Confinement Potentials and the Generation of Numerical Atomic OrbitalsWe aim to develop novel reusable open source infrastructure [Lehtola, J. Chem. Phys. 159, 180901 (2023)] for numerical atomic orbitals (NAOs). Soft confinement potentials are typically used to force…Hugo Åström, Susi Lehtola·May 14, 2025SaveLearn
Unified gas-kinetic wave-particle method for multi-scale phonon transportOver the past 7 decades, the classical Monte Carlo method has played a huge role in the fields of rarefied gas flow and micro/nano scale heat transfer, but it also has shortcomings: the time step and…Hongyu Liu, Xiaojian Yang, Chuang Zhang et al.·May 14, 2025SaveLearn
Unifying same- and different-material particle charging through stochastic scalingTriboelectric charging of insulating particles through contact is critical in diverse physical and engineering processes, from dust storms and volcanic eruptions to industrial powder handling.…Holger Grosshans, Gizem Ozler, Simon Jantač·May 14, 2025SaveLearn
Autoencoder-based Dimensionality Reduction for Accelerating the Solution of Nonlinear Time-Dependent PDEs: Transport in Porous Media with ReactionsPhysics-based models often involve large systems of parametrized partial differential equations, where design parameters control various properties. However, high-fidelity simulations of such systems…Diba Behnoudfar·May 14, 2025SaveLearn
A Fourier finite volume approach for the optical inverse problem of quantitative photoacoustic tomographyA new approach for solving the optical inverse problem of quantitative photoacoustic tomography is introduced, which interpolates between the well-known diffusion approximation and a radiative…David J. Chappell·May 13, 2025SaveLearn
Matched Asymptotic Expansions-Based Transferable Neural Networks for Singular Perturbation ProblemsIn this paper, by utilizing the theory of matched asymptotic expansions, an efficient and accurate neural network method, named as "MAE-TransNet", is developed for solving singular perturbation…Zhequan Shen, Lili Ju, Liyong Zhu·May 13, 2025SaveLearn
Aitomia: Your Intelligent Assistant for AI-Driven Atomistic and Quantum Chemical SimulationsWe have developed Aitomia - a platform powered by AI to assist in performing AI-driven atomistic and quantum chemical (QC) simulations. This evolving intelligent assistant platform is equipped with…Jinming Hu, Hassan Nawaz, Yi-Fan Hou et al.·May 13, 2025SaveLearn
H-HIGNN: A Scalable Graph Neural Network Framework with Hierarchical Matrix Acceleration for Simulation of Large-Scale Particulate SuspensionsWe present a fast and scalable framework, leveraging graph neural networks (GNNs) and hierarchical matrix (H-matrix) techniques, for simulating large-scale particulate suspensions, which…Zhan Ma, Zisheng Ye, Ebrahim Safdarian et al.·May 13, 2025SaveLearn
Simulating many-engine spacecraft: Exceeding 1 quadrillion degrees of freedom via information geometric regularizationWe present an optimized implementation of the recently proposed information geometric regularization (IGR) for unprecedented scale simulation of compressible fluid flows applied to multi-engine…Benjamin Wilfong, Anand Radhakrishnan, Henry Le Berre et al.·May 12, 2025SaveLearn
Fast recovery of parametric eigenvalues depending on several parameters and location of high order exceptional pointsA numerical algorithm is proposed to deal with parametric eigenvalue problems involving non-Hermitian matrices and is exploited to find location of defective eigenvalues in the parameter space of…Benoit Nennig, Martin Ghienne, Emmanuel Perrey-Debain·May 9, 2025SaveLearn
Computational Homogenization in 3D Magnetostatics using E3C Hyper-ReductionThe recently published hyper-reduction method "Empirically Corrected Cluster Cubature" (E3C) is for the first time applied in three dimensions (here magnetostatics). The method is verified to give…Hauke Goldbeck, Stephan Wulfinghoff·May 9, 2025SaveLearn
A LightGBM-Incorporated Absorbing Boundary Conditions for the Wave-Equation-Based Meshless MethodA LightGBM-Incorporated absorbing boundary condition (ABC) computation approach for the wave-equation-based the radial point interpolation meshless (RPIM) method is proposed to simulate wave…Qi-Chang Dong, Zhizhang David Chen·May 9, 2025SaveLearn
Fast and Fourier Features for Transfer Learning of Interatomic PotentialsTraining machine learning interatomic potentials that are both computationally and data-efficient is a key challenge for enabling their routine use in atomistic simulations. To this effect, we…Pietro Novelli, Giacomo Meanti, Pedro J. Buigues et al.·May 8, 2025SaveLearn
BraWl: Simulating the thermodynamics and phase stability of multicomponent alloys using conventional and enhanced sampling techniquesWe present BraWl, a Fortran package implementing a range of conventional and enhanced sampling algorithms for exploration of the phase space of the Bragg-Williams model, facilitating study of…Hubert J. Naguszewski, Livia B. Pártay, David Quigley et al.·May 8, 2025SaveLearn
A two-sided subgrid-scale model for mass transfer across fluid interfacesThe occurrence of extremely thin concentration boundary layers at fluid interfaces for high local P\'eclet numbers is a severe obstacle for efficient and accurate numerical simulation of mass…Moritz Schwarzmeier, Tomislav Marić, Željko Tuković et al.·May 8, 2025SaveLearn
Structure determination from single-molecule X-ray scattering images using stochastic gradient ascentScattering experiments using ultrashort X-ray free electron laser (XFEL) pulses have opened a new path for structure determination of a wide variety of specimens, including nano-crystals and entire…Steffen Schultze, D. Russell Luke, Helmut Grubmüller·May 8, 2025SaveLearn
Efficient construction of effective Hamiltonians with a hybrid machine learning methodThe effective Hamiltonian method is a powerful tool for simulating large-scale systems across a wide range of temperatures. However, previous methods for constructing effective Hamiltonian models…Yang Cheng, Binhua Zhang, Xueyang Li et al.·May 8, 2025SaveLearn
Is the end of Insight in Sight ?The rise of deep learning challenges the longstanding scientific ideal of insight - the human capacity to understand phenomena by uncovering underlying mechanisms. In many modern applications,…Jean-Michel Tucny, Mihir Durve, Sauro Succi·May 7, 2025SaveLearn
Adaptive Equilibration of Molecular Dynamics SimulationsWe present a systematic framework for shortening and automating molecular dynamics equilibration through improved position initialization methods and uncertainty quantification analysis, using the…Luciano G. Silvestri, Zachary A. Johnson, Michael S. Murillo·May 7, 2025SaveLearn
Linear Analysis of Stochastic Verlet-Type Integrators for Langevin EquationsWe provide an analytical framework for analyzing the quality of stochastic Verlet-type integrators for simulating the Langevin equation. Focusing only on basic objective measures, we consider the…Niels Grønbech-Jensen·May 7, 2025SaveLearn
Multiphysics Modeling of SNAP 10A/2 Space Reactor with CardinalThe SNAP 10/A nuclear-powered satellite was launched into space in 1965. The present work discusses the development of a coupled neutronic-thermal hydraulics model with the high-fidelity multiphysics…Maximiliano Dalinger, Elia Merzari, Tri Nguyen et al.·May 6, 2025SaveLearn
Dilatation-driven spurious dissipation in weakly compressible methodsThe weakly compressible methods to simulate incompressible flows are in a state of rapid development, owing to the envisaged efficiency they offer for parallel computing. The pressure waves in such…Dheeraj Raghunathan, Y. Sudhakar·May 6, 2025SaveLearn
A Data-Driven Method for Modeling Creep-Fatigue Stress-Strain Behavior Using Neural ODEsIn this paper, we introduce a data-driven machine learning approach for modeling one-dimensional stress-strain behavior under cyclic loading, utilizing experimental data from the nickel-based Alloy…Hao Deng, Mark C. Messner·May 5, 2025SaveLearn
ACCES: Non-Invasive Simulation Calibration via Optimisation using Evolutionary Algorithms and MetaprogrammingCalibration is a vital step in the development of rigorous digital models of diverse physical and chemical processes, yet one which is highly time- and labour-intensive. In this paper, we introduce a…Andrei-Leonard Nicusan, Dominik Werner, Jack A. Sykes et al.·May 5, 2025SaveLearn
Node pruning reveals compact and optimal substructures within large networksThe structural complexity of reservoir networks poses a significant challenge, often leading to excessive computational costs and suboptimal performance. In this study, we introduce a systematic,…Manish Yadav, Merten Stender·May 5, 2025SaveLearn