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 presents and numerically validates a computational method for thermal radiative transfer that couples multilevel quasidiffusion moment equations with the method of long characteristics for the Boltzmann transport equation, demonstrating its accuracy and convergence through independent mesh refinement studies on the Fleck-Cummings test problem.
This paper introduces R-ChFSI, a residual-based reformulation of Chebyshev Filtered Subspace Iteration that ensures robust convergence for large-scale sparse Hermitian eigenvalue problems even when using inexact matrix-vector products, approximate inverses, and reduced-precision arithmetic, thereby achieving significant speedups on GPU accelerators while maintaining high accuracy.
This paper introduces a quantum protocol combining reverse quantum annealing with optimized nonequilibrium initial distributions to efficiently estimate Ising partition functions at low temperatures, significantly reducing computational scaling exponents and overcoming the statistical fluctuations that limit classical methods while remaining feasible for near-term quantum devices.
This paper proposes an enhanced data-driven reduced-order model combining autoencoders and neural ordinary differential equations with a novel latent gradient loss term, demonstrating significantly improved accuracy, robustness, and computational efficiency for simulating stiff hydrogen and ammonia-air ignition dynamics compared to traditional methods.
FFTArray is a modular, open-source Python library built on the Array API Standard that simplifies the implementation of discretized multi-dimensional Fourier transforms for pseudo-spectral methods by automating complex grid and scaling corrections while ensuring compatibility with diverse backends like NumPy, JAX, and PyTorch.
This paper introduces a unified variational framework that integrates phase-field fracture and third-medium contact within finite deformation hyperelasticity by regularizing both crack topology and contact interfaces, thereby eliminating the need for explicit tracking algorithms while successfully simulating complex coupled phenomena like secondary crushing in Brazilian disk tests.
This paper presents an optimized protocol using artificial neural networks to identify solvents in starch-liquid slurries from post-desiccation fracture patterns with 96% accuracy by analyzing nine morphological features, particularly crack area distribution.
This paper proposes a qudit-based formulation of the Rodeo algorithm featuring a novel "Rodeo kernel" spectral filter and a microcanonical protocol for estimating entropic quantities, demonstrating through numerical simulations on the Ising model that higher-dimensional ancillae significantly reduce fluctuations and enhance spectral analysis compared to traditional qubit implementations.
This study demonstrates that engineering nitrogen termination on diamond surfaces significantly enhances Cu/diamond interfacial thermal conductance by 21% through surficial mass modification and bonding regulation that selectively modulates high-frequency phonon transport, offering a promising non-metallic strategy to overcome interfacial limitations in Cu-diamond composites.
Aitomia is an AI-powered intelligent assistant platform that integrates large language model agents with the MLatom software to democratize and accelerate atomistic and quantum chemical simulations by enabling both experts and non-experts to autonomously set up, run, and analyze complex computational workflows through a user-friendly chat interface.