1242 papers
Computational physics bridges the gap between abstract theory and real-world observation by using powerful computers to solve complex physical problems. This field allows scientists to simulate everything from the collision of subatomic particles to the swirling dynamics of galaxies, offering insights that traditional experiments alone cannot provide.
On Gist.Science, we continuously process every new preprint in this category from arXiv to make these breakthroughs accessible to everyone. Each entry is accompanied by both a clear, plain-language explanation and a detailed technical summary, ensuring that researchers and curious readers alike can grasp the significance of the latest findings without getting lost in dense equations.
Below are the latest papers in computational physics, curated to keep you at the forefront of this rapidly evolving discipline.
This study utilizes GRAS/Geant4 simulations to characterize background count rates in low Earth orbiting Cherenkov detectors, demonstrating that while coincidence techniques effectively mitigate trapped particle interference to enable detailed spectral analysis of Ground-Level Enhancements, significant background rates persist in the South Atlantic Anomaly.
This study characterizes the stability of continuous-time quantum walks across diverse network topologies under various decoherence models, revealing that while dense and heterogeneous networks exhibit robustness against certain noise types, they suffer rapid decay under edge-based stochastic processes and face a fundamental trade-off between structural localization and coherence preservation that is critically dependent on the initialization node's centrality.
This paper demonstrates that transfer learning with Generative Adversarial Networks effectively extrapolates physics information from synthetic neutrino-carbon scattering data to related processes like neutrino-argon and antineutrino-carbon interactions, significantly outperforming models trained from scratch and maintaining high accuracy even with limited statistics.
Using advanced population annealing Monte Carlo simulations to overcome metastability issues, this study demonstrates that the frustrated Ising model on a honeycomb lattice undergoes a continuous second-order phase transition within the Ising universality class for all , correcting previous misinterpretations of first-order behavior caused by long-lived metastable states.
This paper proposes a gradient-enhanced nonlocal continuum framework that extends anisotropic hyperelasticity to robustly model microstructural instabilities, phase transformations, and complex macroscopic behaviors in architected metamaterials, overcoming the scalability limitations of conventional discrete approaches.
This study employs an ab initio renormalized random-phase approximation framework with modern chiral three-body forces to successfully calculate bulk and spectroscopic properties of closed-shell nuclei, demonstrating improved agreement with experiments by eliminating quasiboson approximation instabilities while highlighting the necessity of extending beyond the particle-hole space to resolve remaining discrepancies.
QMCkl is a modular, high-performance C-compatible library that accelerates Quantum Monte Carlo calculations by providing portable, optimized kernels for essential operations while ensuring numerical consistency and cross-code interoperability.
This paper presents a graph partitioning-based algorithm that solves large-scale nonlinear network flow equations by decomposing the system into smaller, locally solvable subsystems connected at interconnects, thereby enabling efficient computation while preserving data privacy for different network operators.
The authors present a new multigroup radiation diffusion module for the RICH moving-mesh code, validate it against analytic benchmarks, and apply it to simulate a tidal disruption event that successfully reproduces an early X-ray flash consistent with observational data.
This paper introduces Generative Replica Exchange (GREX), a novel framework that integrates deep generative models and normalizing flows into replica exchange simulations to eliminate the need for multiple intermediate temperature replicas, thereby significantly accelerating molecular sampling while maintaining thermodynamic rigor.