1201 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.
The paper introduces SesQ, a highly efficient surface integral equation simulator that overcomes the computational bottlenecks of conventional finite element methods to enable precise, rapid calculation of the energy participation ratio for optimizing low-loss superconducting qubit designs.
This paper introduces a fully automated, gradient-based framework that efficiently extracts interatomic force constants directly from X-ray thermal diffuse scattering data by bypassing traditional computational bottlenecks, thereby enabling high-throughput refinement of lattice dynamics models.
This paper presents a scalable monolithic modified Newton multigrid framework for solving fully implicit space-time discretizations of time-dependent -Navier-Stokes equations, demonstrating robustness and efficiency across shear-thinning regimes and various model parameters through numerical tests.
This paper presents the implementation and evaluation of an asynchronous discontinuous Galerkin method with asynchrony-tolerant fluxes in the deal.II library, demonstrating that this approach recovers high-order accuracy for compressible flow simulations while achieving significant speedups (up to 1.9x) by reducing synchronization overheads in large-scale parallel computing.
This paper introduces version 2.0 of SmoQyDQMC.jl, a flexible Julia package that implements the determinant quantum Monte Carlo algorithm to simulate Hubbard and generalized electron-phonon interactions using an optimized hybrid Monte Carlo method with exact forces for efficient phonon sampling.
This molecular dynamics study demonstrates that the creep resistance of MoNbTaW refractory high-entropy alloys is significantly enhanced by increasing grain size and introducing local chemical order, as both factors strengthen grain boundaries and suppress grain-boundary-dominated deformation mechanisms.
This paper proposes a surrogate-based optimization method using radial basis function interpolation to efficiently train parameterized quantum circuits with minimal quantum hardware calls, demonstrating superior performance on Max-Cut and Ising models compared to state-of-the-art techniques without requiring upfront classical training.
This paper introduces MC3D, a comprehensive online database on the Materials Cloud portal containing experimentally known stoichiometric inorganic crystal structures with fully reproducible, DFT-optimized geometries derived from automated workflows and curated input protocols.
The paper introduces GoodRegressor, a hierarchical symbolic regression framework that balances predictive performance and interpretability by using depth-controlled expansion to navigate vast compositional spaces, achieving state-of-the-art results in materials science while revealing system-specific optimal interaction depths.
This paper employs Bayesian optimization combined with advanced particle-in-cell simulations to refine the matching conditions for self-guided laser wakefield accelerators, demonstrating that maximum electron energy can be achieved across a wide range of input parameters without precise tuning, thereby significantly relaxing operational constraints for experimental implementation.