922 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 paper introduces QuVI, an open-source, native LabVIEW toolkit that leverages the dataflow paradigm to provide an intuitive, graphical environment for simulating quantum circuits, bridging the gap between abstract linear algebra and practical engineering implementation for educators and researchers.
This paper derives a general thermodynamic driving force for transformation fronts in deformable ferromagnets and adapts the cut-finite-element method to efficiently model the coupled magneto-mechanical behavior and propagating interfaces of magnetic shape-memory alloys without requiring mesh modifications.
This study demonstrates that point-like defects significantly stabilize square-triangle quasicrystals in soft-matter systems by providing a substantial entropy gain through both individual contributions and combinatorial mixing, thereby explaining the high defect concentrations observed in these materials.
The Kratos framework introduces novel, heterogeneous-architecture-optimized algorithms for ray tracing, reacting flow, and thermochemistry, demonstrating high accuracy and efficiency through rigorous verification against semi-analytic solutions and established benchmarks like Cantera.
The paper introduces EquiNO, a physics-informed neural operator that hard-enforces equilibrium constraints via divergence-free basis functions to provide a robust, 8000-fold faster alternative to traditional multiscale simulations and existing data-driven surrogates.
This paper extends efficient Alternative Finite Difference WENO (AFD-WENO) schemes to divergence-preserving hyperbolic systems, such as CED and MHD, by retaining a Yee-style collocation of variables to handle involution constraints that were previously only solvable with higher-order finite volume methods.
This paper demonstrates that combining sparse grid interpolation with (L)-Leja points and optimized dynamic mode decomposition enables the construction of highly efficient, predictive parametric reduced-order models for complex plasma instabilities, achieving evaluation speeds up to three orders of magnitude faster than high-fidelity simulations while requiring only a minimal number of training data points.
This paper proposes a functional information framework to quantify classical objectivity in Quantum Darwinism by measuring the abundance of environment fragments that redundantly encode pointer information, revealing thermodynamic constraints where each additional bit of objectivity doubles the minimal heat dissipation required for record stabilization.
This study employs first-principles calculations and device simulations to demonstrate that lead-free () perovskites possess the necessary dynamical stability, tunable optoelectronic properties, and suitable band gaps to serve as promising candidates for stable thin-film PIN photodiode applications.
This paper presents a novel framework combining high-fidelity Monte Carlo simulations with Bayesian inference to achieve rapid, high-accuracy quantification of mobile gamma-ray sources, significantly advancing capabilities in radiological safety, geophysical mapping, and space exploration.