NAS-PINNv2: Improved neural architecture search framework for physics-informed neural networks in low-temperature plasma simulationLimited by the operation and measurement conditions, numerical simulation is often the only feasible approach for studying plasma behavior and mechanisms. Although artificial intelligence methods,…Yifan Wang, Linlin Zhong·Jan 25, 2025SaveLearn
Stochastic Method for Delayed Neutron Precursors Transport in Liquid FuelThis paper presents a novel stochastic method for modeling the transport of Delayed Neutron Precursors (DNPs) in liquid nuclear fuel. The method incorporates advection and diffusion effects into the…Mathis Caprais, Daniele Tomatis·Jan 24, 2025SaveLearn
E3C for Computational Homogenization in Nonlinear MechanicsIn computational homogenization, a fast solution of the microscopic problem can be achieved by model order reduction in combination with hyper-reduction. Such a technique, which has recently been…Stephan Wulfinghoff, Jan Hauck·Jan 23, 2025SaveLearn
A Parallel Block Preconditioner-Based VIE-FFT Algorithm for Modeling the Electromagnetic Response From NanostructuresThe superior ability of nanostructures to manipulate light has propelled extensive applications in nano-electromagnetic components and devices. Computational electromagnetics plays a critical role in…Chengnian Huang, Wei E. I. Sha·Jan 23, 2025SaveLearn
Solving continuum and rarefied flows using differentiable programmingAccurate and efficient prediction of multi-scale flows remains a formidable challenge. Constructing theoretical models and numerical methods often involves the design and optimization of parameters.…Tianbai Xiao·Jan 23, 2025SaveLearn
Reproducibility of fixed-node diffusion Monte Carlo across diverse community codes: The case of water-methane dimerFixed-node diffusion quantum Monte Carlo (FN-DMC) is a widely-trusted many-body method for solving the Schr\"odinger equation, known for its reliable predictions of material and molecular…Flaviano Della Pia, Benjamin X. Shi, Yasmine S. Al-Hamdani et al.·Jan 22, 2025SaveLearn
Recognizing and generating knotted molecular structures by machine learningKnotted molecules occur naturally and are designed by scientists to gain special biological and material properties. Understanding and utilizing knotting require efficient methods to recognize and…Zhiyu Zhang, Yongjian Zhu, Liang Dai·Jan 22, 2025SaveLearn
On the practical applicability of modern DFT functionals for chemical computations. Case study of DM21 applicability for geometry optimizationDensity functional theory (DFT) is probably the most promising approach for quantum chemistry calculations considering its good balance between calculations precision and speed. In recent years,…Kirill Kulaev, Alexander Ryabov, Michael Medvedev et al.·Jan 21, 2025SaveLearn
Numerical Modeling of Oxygen Diffusion in Tissue Spheroids Undergoing Fusion Using Functional Representation and Finite VolumesA three-dimensional cell culture called a spheroid serves as a foundational entity in a wide variety of modern tissue engineering applications, including 3D-bioprinting and preclinical drug testing.…Katherine Vilinski-Mazur, Bogdan Kirillov, Oleg Rogozin et al.·Jan 21, 2025SaveLearn
Extreme scaling of the metadynamics of paths algorithm on the pre-exascale JUWELS Booster supercomputerMolecular dynamics (MD)-based path sampling algorithms are a very important class of methods used to study the energetics and kinetics of rare (bio)molecular events. They sample the highly…Nitin Malapally, Marta Devodier, Giulia Rossetti et al.·Jan 21, 2025SaveLearn
Nested Sampling for Exploring Lennard-Jones ClustersLennard-Jones clusters, while an easy system, have a significant number of non equivalent configurations that increases rapidly with the number of atoms in the cluster. Here, we aim at determining…Lune Maillard, Fabio Finocchi, César Godinho et al.·Jan 20, 2025SaveLearn
Numerical evolution of the resistive relativistic magnetohydrodynamic equations: a minimally implicit Runge-Kutta schemeWe present the Minimally-Implicit Runge-Kutta (MIRK) methods for the numerical evolution of the resistive relativistic magnetohydrodynamic (RRMHD) equations, following the approach proposed by…Isabel Cordero-Carrión, Samuel Santos-Pérez, Clara Martínez-Vidallach·Jan 19, 2025SaveLearn
Multiscale simulation of rarefied polyatomic gas flow via DIG methodA novel multiscale numerical method is developed to accelerate direct simulation Monte Carlo (DSMC) simulations for polyatomic gases with internal energy. This approach applies the general synthetic…Liyan Luo, Tao Huang, Qi Li et al.·Jan 19, 2025SaveLearn
Random batch sum-of-Gaussians algorithm for molecular dynamics simulations of Yukawa systems in three dimensionsYukawa systems have drawn widespread interest across various applications. In this paper, we introduce a novel random batch sum-of-Gaussians (RBSOG) algorithm for molecular dynamics simulations of 3D…Chen Chen, Jiuyang Liang, Zhenli Xu·Jan 19, 2025SaveLearn
Deep variational free energy prediction of dense hydrogen solid at 1200KWe perform deep variational free energy calculations to investigate the dense hydrogen system at 1200 K and high pressures. In this computational framework, neural networks are used to model the free…Xinyang Dong, Hao Xie, Yixiao Chen et al.·Jan 16, 2025SaveLearn
Singular Spectrum Analysis of Time-series Data from Time-dependent density-functional theory in Real-timeThis paper introduces a spectral analysis of time-seires data derived from real-time time-dependent density functional theory (TDDFT) using Singular Spectrum Analysis (SSA). TDDFT is a robust method…Naoki Tani, Satoru S. Kano, Yasunari Zempo·Jan 16, 2025SaveLearn
Physics-Aware POD-Based Learning for Ab initio QEM-Galerkin Simulations of Periodic NanostructuresQuantum nanostructures offer crucial applications in electronics, photonics, materials, drugs, etc. For accurate design and analysis of nanostructures and materials, simulations of the Schrodinger or…Martin Veresko, Yu Liu, Daqing Hou et al.·Jan 15, 2025SaveLearn
Reinforcement learning-based adaptive time-integration for nonsmooth dynamicsNumerical time integration is fundamental to the simulation of initial and boundary value problems. Traditionally, time integration schemes require adaptive time-stepping to ensure computational…David Michael Riley, Alexandros Stathas, Diego Gutiérrez-Oribio et al.·Jan 15, 2025SaveLearn
Comparison of stochastic BGK and FP methods for the simulation of non-equilibrium multi-species molecular gas flowsDue to limited possibilities of experimental investigations for non-equilibrium gas flows, numerical results are of highest interest. Although the well-established Direct Simulation Monte Carlo…Franziska Hild, Marcel Pfeiffer·Jan 15, 2025SaveLearn
Surrogate-based multilevel Monte Carlo methods for uncertainty quantification in the Grad-Shafranov free boundary problemWe explore a hybrid technique to quantify the variability in the numerical solutions to a free boundary problem associated with magnetic equilibrium in axisymmetric fusion reactors amidst parameter…Howard Elman, Jiaxing Liang, Tonatiuh Sánchez-Vizuet·Jan 14, 2025SaveLearn
Non-autonomous standard nontwist mapArea-preserving nontwist maps locally violate the twist condition, giving rise to shearless curves. Nontwist systems appear in different physical contexts, such as plasma physics, climate physics,…Marcos V. de Moraes, Iberê L. Caldas, Yves Elskens·Jan 14, 2025SaveLearn
Repulsive interatomic potentials calculated at three levels of theoryThe high-energy repulsive interaction between nuclei at distances much smaller than the equilibrium bond length is the key quantity determining the nuclear stopping power and atom scattering in keV…Kai Nordlund, Susi Lehtola, Gerhard Hobler·Jan 11, 2025SaveLearn
Efficient stochastic simulation of piecewise-deterministic Markov processes and its application to the Morris-Lecar model of neural dynamicsPiecewise-deterministic Markov processes combine continuous in time dynamics with jump events, the rates of which generally depend on the continuous variables and thus are not constants. This leads…Arkady Pikovsky·Jan 11, 2025SaveLearn
Derivative Source Method for Monte Carlo Transport Calculation of Sensitivities to Material Densities and DimensionsThe Derivative Source Method (DSM) takes derivatives of a particle transport equation with respect to selected parameters and solves them via the standard Monte Carlo random walk simulation along…Ilham Variansyah, Ryan G. McClarren, Todd S. Palmer·Jan 11, 2025SaveLearn
Implicit Collision Multiplicity Adjustment for Efficient Monte Carlo Transport Simulation of Reactivity ExcursionWe present an implicit collision method with on-the-fly multiplicity adjustment based on the forward weight window methodology for efficient Dynamic Monte Carlo (MC) simulation of reactivity…Ilham Variansyah, Ryan G. McClarren, Todd S. Palmer·Jan 11, 2025SaveLearn