1258 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 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.
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 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 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 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 introduces "differentiable microscopy" (), a data-driven, top-down design framework that automatically optimizes optical systems for phase retrieval, demonstrating superior performance over existing methods and experimentally validating its effectiveness on biological samples.
This paper introduces a constant-lag Neural Delay Differential Equations (NDDEs) framework, inspired by the Mori-Zwanzig formalism, to effectively learn non-Markovian dynamics from partially observed data by identifying memory effects through time delays, demonstrating superior performance over existing methods like LSTMs and ANODEs across synthetic, chaotic, and experimental datasets.
This paper extends the Theory of Porous Media to model non-isothermal material injection into porous structures, specifically for percutaneous vertebroplasty, by incorporating local thermal non-equilibrium conditions and demonstrating thermodynamic consistency through numerical simulations.
This paper presents an extended non-affine deformation theory incorporating a time-dependent memory kernel within the Generalized Langevin Equation, which successfully predicts the viscoelastic response of poly(methyl methacrylate) across twenty frequency decades and validates these findings against diverse experimental and computational methods.
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.