905 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 proposes and demonstrates a multi-outcome optimization strategy for photonic quantum circuits that significantly enhances the success probability of generating non-Gaussian states by leveraging multiple useful measurement patterns and aggregating degenerate outcomes, rather than restricting optimization to a single target outcome.
This study utilizes Direct Numerical Simulations to demonstrate that thermodiffusive instabilities in turbulent lean premixed hydrogen-air flames significantly enhance low-frequency combustion noise by altering heat release fluctuations and flame surface dynamics through the coupled action of turbulence and flame stretch, distinguishing their acoustic behavior from stable methane flames.
This paper presents a stable and efficient numerical method for solving acoustic multibody scattering problems in two and three dimensions by combining local Method of Fundamental Solutions (MFS) approximations with a global iterative solver, achieving high accuracy and scalability without the implementation complexity of traditional boundary integral discretization techniques.
This study predicts that phosphorus carbide nanotubes (NTs) are a stable, chemically realistic quasi-one-dimensional material that uniquely hosts coexisting Dirac fermions and robust flat bands at the Fermi level, while exhibiting strain-induced structural and quantum phase transitions, localized edge states, and tunable magnetism for potential applications in quantum hardware and spintronics.
This paper introduces Effective Field Neural Networks (EFNNs), a novel architecture leveraging continued functions from renormalization theory to accurately model classical and quantum many-body systems with superior generalization to larger lattice sizes and significant computational speedups compared to exact diagonalization and standard deep learning models.
This paper employs Information Imbalance to demonstrate that semantic information converges across languages, modalities, and architectures in deep models, revealing that predictability is strongest in specific central or final layers and that larger, independently trained models can outperform jointly trained multimodal models in cross-modal alignment.
This perspective article proposes block-encodings and polynomial transformations as a unified, scalable framework for quantum computational science, bridging the gap between abstract algorithmic theory and practical applications in chemistry, physics, and optimization.
This paper introduces a flux-correction form of the third-order edge-based scheme that enables the direct application of general numerical flux functions while preserving third-order accuracy through the use of the U-MUSCL scheme with , as verified by numerical results on irregular tetrahedral grids.
This paper proposes a scalable machine learning framework for classification and pattern recognition that leverages ensembles of coupled chaotic oscillators, where a neural network automatically learns the necessary coupling terms to induce local resonances for data processing, thereby eliminating the need for expert-designed coupling rules and enabling efficient gradient-based optimization.
This paper proposes a data-efficient statistical framework that quantifies discrepancies in molecular dynamics simulations by measuring distances between covariance matrices, enabling the extraction of low-dimensional features that effectively correlate with global physical properties like diffusion coefficients and distinguish between different phases such as ice and liquid water.