917 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 a computationally efficient, open-source method that utilizes a matrix-valued adaptive Antoulas-Anderson (AAA) algorithm to represent the frequency-dependent T-matrix as a pole-expansion, thereby overcoming the high memory and computational costs of traditional discrete frequency sampling while preserving physical interpretability.
This paper benchmarks the nested_fit program, which utilizes nested sampling and slice sampling, by successfully determining the partition functions and identifying phase transitions and stable configurations for 7-atom and 36-atom Lennard-Jones clusters while highlighting the algorithm's impact on computational cost.
The paper introduces GollumFit, an open-source framework designed to efficiently perform binned-likelihood analyses on IceCube neutrino telescope data by incorporating both general and specific model parameters to handle complex fitting scenarios with numerous nuisance parameters.
This paper introduces QHFlow2, a state-of-the-art machine learning Hamiltonian model that significantly outperforms existing methods in energy and force prediction accuracy by directly evaluating predicted Hamiltonians, achieving NequIP-level force precision and up to 20-fold improvements in energy error on standard benchmarks.
This paper demonstrates that flow matching in a lower-dimensional latent space, enhanced by explicit or implicit geometric regularization, enables fast, single-step stochastic closure modeling for complex dynamical systems like 2D Kolmogorov flows while preserving physical fidelity and requiring minimal training data.
This paper introduces a novel, high-order, and conservative discontinuous Galerkin method for solving the relativistic Vlasov–Maxwell system that eliminates Monte-Carlo noise and utilizes a flexible velocity-space mapping to efficiently model diverse extreme high-energy-density plasma phenomena.
This paper compares simulated annealing and evolutionary algorithms, both utilizing SMILES string representations, and finds them to be equally effective for discovering molecules with large molecular average hyperpolarizabilities for nonlinear optical applications.
This paper introduces a combined dynamic-kinematic validation framework and a novel diagram to demonstrate that relying solely on maximum spreading diameter is insufficient for accurate droplet impact modeling, advocating instead for a hybrid contact angle model that better captures both geometric spreading limits and internal kinematic receding dynamics.
This paper presents a fluctuating lattice Boltzmann formulation based on orthogonal central moments that introduces stochastic forcing directly in the central moment space to exactly satisfy the fluctuation-dissipation theorem, ensuring thermal equilibrium, Galilean invariance, and enhanced numerical stability even in the over-relaxation regime.
This study combines experimental characterization and crystal plasticity modeling to demonstrate that increasing yttrium content in extruded Mg alloys suppresses common TT1 tension twins while promoting rare TT2 twins, alters critical resolved shear stress ratios, and induces higher local strain accumulation at TT2 sites, thereby offering new insights for alloy design.