119 papers
This paper presents and validates a generalized algorithm for efficient Monte-Carlo sampling of metastable systems using non-local updates in collective-variable space under underdamped Langevin dynamics, demonstrating substantial performance improvements over previous overdamped approaches and extending the applicability of machine-learning-based samplers to more realistic molecular systems.
This paper demonstrates that the Transient Time Correlation Function (TTCF) method is a computationally efficient and precise alternative to traditional time-averaging approaches for calculating nonequilibrium transport coefficients in both linear and nonlinear regimes, as validated through case studies of the Lorentz gas and anharmonic oscillator chains.
This paper presents a robust, GEMM-based direct solver for finite-difference Poisson problems on non-uniform 3D Cartesian grids that leverages tensor formulations and matrix-matrix multiplications to achieve superior time-to-solution and parallel efficiency compared to traditional multigrid and FFT-based methods on modern heterogeneous hardware.
This study establishes a high-fidelity finite-element model of the Chang'e-7 lander to demonstrate that extreme lunar south-pole thermal cycles, primarily affecting solar array stiffness, cause significant drift in the lander's fundamental resonance frequency (0.64–0.87 Hz), which critically overlaps with the seismic observation window and necessitates specific noise filtering strategies for accurate interior probing.
This paper demonstrates that combining highly accurate machine-learned potentials with the quantum thermal bath method provides a computationally efficient and reliable approach for simulating the infrared spectra of protonated water clusters, offering a cost-effective alternative to traditional quantum dynamics techniques.
This study utilizes volume of fluid simulations to investigate droplet impact on random hydrophobic surfaces, revealing that while maximum spreading decreases linearly with increasing roughness and contact time remains constant, the interplay between Weber number and surface roughness governs wetting-bouncing transitions and delays bouncing with larger roughness.
This paper proposes a two-dimensional multi-phase-field model based on Puck failure theory and a mesh overlay method to accurately predict and simulate various in-plane damage modes in fiber-reinforced composite laminates, demonstrating strong agreement with experimental results across multiple benchmark loading scenarios.
This paper numerically investigates how external magnetic fields influence the long-range transport of large polarons (solitons) in discrete quasi-one-dimensional systems, demonstrating that the effect depends on both field strength and specific system parameters that define soliton properties, while also examining polarons generated by donor complexes.
This paper provides a rigorous theoretical justification for the noise-cancellation algorithm in interacting Brownian systems by proving that cross-correlations vanish in thermal equilibrium—rendering the method exact—while demonstrating that finite cross-correlations in nonequilibrium systems serve as a distinct fingerprint of non-equilibrium physics requiring specific corrections.
This paper presents a comprehensive analysis and the first open-source implementation of phase-shifting dealiasing methods for pseudo-spectral simulations of incompressible Navier-Stokes equations, demonstrating that these techniques can achieve up to a threefold speedup over standard 2/3 truncation with minimal accuracy loss.
This paper introduces a robust, high-order numerical framework for simulating next-generation solar cells that combines structure-preserving spatial discretization with L-stable implicit Runge-Kutta time integration to accurately model coupled charge, exciton, and ion transport dynamics without empirical parameters.
The paper introduces **unxt**, a Python package built on the **quax** framework that integrates **astropy.units** with **JAX** to enable seamless, high-performance, unit-aware scientific computing.
This paper demonstrates that simulation-based inference (SBI) is a viable and potentially superior alternative to traditional empirical tuning for determining neutrino interaction model parameters, as it successfully reproduces and slightly improves upon the MicroBooNE collaboration's tuned GENIE configuration while also approximating the NuWro simulation.
This paper benchmarks various parallelization strategies for flat-histogram Monte Carlo sampling and demonstrates that non-uniform energy domain decomposition offers the most significant performance improvements, providing concrete recommendations for optimizing simulations of complex alloy systems.
This paper introduces PF-PINO, a physics-informed neural operator framework that embeds phase-field governing equation residuals into the training loss to significantly improve the accuracy, generalization, and long-term stability of predictive parametric phase-field modelling compared to conventional methods.
This paper introduces an efficient framework combining the Clausius-Clapeyron equation with the quasi-harmonic approximation to calculate low-temperature phase boundaries with minimal computational cost, demonstrating its accuracy and versatility by constructing the silica phase diagram using both density functional theory and machine-learned interatomic potentials.
This paper demonstrates that the fully relativistic fixed spin moment (FR-FSM) method reconciles discrepancies in magnetocrystalline anisotropy energy (MAE) calculations arising from different exchange-correlation potentials and provides a framework for estimating maximum MAE values to guide the design of new-generation permanent magnets.
By combining machine learning interatomic potentials with specialized loop updates to overcome high energy barriers and ensure accurate sampling, this study successfully simulates the first-order proton-ordering transition in water ice at 83 K, a result that aligns with experimental values after accounting for nuclear quantum effects.
This paper introduces a novel tensor-network methodology that combines real-space Bethe-Salpeter Hamiltonian encoding with a Chebyshev algorithm to efficiently compute excitonic spectra and bound-exciton spectral functions in super-moiré systems exceeding one billion lattice sites, thereby overcoming the computational limitations of conventional approaches for large-scale quantum matter.
This paper introduces a hybrid Monte Carlo algorithm for global time-dependent particle transport that utilizes automatic weight windows derived from a second-order accurate, noise-filtered hybrid MC/deterministic solution of low-order second-moment equations to achieve uniform particle distribution and enhanced computational efficiency.