903 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 fitPALSpectra, an open-source Python workflow that addresses the challenges of analyzing positron annihilation lifetime spectroscopy (PALS) data by providing a configurable tool for simulating, fitting, and visualizing spectra using an analytically integrated exponential–Gaussian model, which has been validated to accurately recover ground-truth parameters on synthetic data.
This paper proposes a mixed Hermite-Legendre spectral method for kinetic plasma simulations that combines the efficiency of Hermite polynomials for near-Maxwellian distributions with the resolution capabilities of Legendre polynomials for localized non-Maxwellian features, achieving improved accuracy and conservation of physical invariants at a comparable computational cost.
This paper introduces a Joint Approximate Diagonalization method for quasiparticle self-consistent $GW$ calculations that utilizes the full dynamical self-energy and a density matrix derived from the full Green's function, achieving accuracy comparable to standard while offering improved agreement with high-level CCSD(T) reference values.
This paper introduces General-Geometry Neural Whitney Forms (Geo-NeW), a data-driven finite element method that jointly learns differential operators and compatible reduced spaces to preserve physical conservation laws and achieve superior generalization to unseen geometries in solving Partial Differential Equations.
This paper establishes a theoretical framework comparing closed-system neural network ensembles with open-system analogs from nuclear reaction theory, ultimately concluding that the latter's distinctive non-Hermitian dynamics are structurally absent in mainstream learning due to the lack of continuous spectra and wave-like behavior, thereby locating the true source of operational uncertainty within the closed-system correspondence.
This paper resolves the paradox of Matrix Product States (MPS) being highly trainable despite the general trainability issues of quantum circuits by proving that the gauge freedom in MPS induces effective local overparametrization, which eliminates poor local minima and concentrates them near the global minimum.
This paper introduces Graphlet-MP, a comprehensive database and open-source toolkit that provides interpretable, data-efficient graphlet histogram representations for over 149,000 inorganic crystals to enable materials property prediction even with scarce experimental data.
This paper introduces Physics-Informed Multivariate Functional Approximation (PI-MFA), a framework that utilizes tensor-product B-splines to generate continuous, differentiable flow field reconstructions by optimizing control points to balance data fidelity with governing physical laws, thereby ensuring physically consistent results even from inconsistent input data.
This paper demonstrates that small, optimally trained LSTM networks exhibit near-critical branching dynamics and scale-free avalanche statistics, while larger models remain subcritical, with a proposed mixture branching process framework explaining how heterogeneous dynamics can still generate robust long-range temporal correlations.
This paper introduces PulsarX, an open-source framework utilizing adaptive Kolmogorov-Arnold networks and automated training pipelines to achieve highly accurate, self-consistent axisymmetric pulsar magnetosphere solutions with significantly improved convergence speed, reduced manual tuning, and the ability to resolve extreme spatial scales compared to previous Physics-Informed Neural Network approaches.