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 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.
This paper introduces a fully local, adaptive repulsive flux within a conservative Allen-Cahn phase-field lattice Boltzmann model to effectively prevent spurious coalescence in multiphase flow simulations by dynamically adjusting interaction strength based on estimated local film thickness, thereby ensuring robust and physically consistent near-contact dynamics without sacrificing computational efficiency.
This paper establishes an analogy between nonlinear photonic meta-atoms and soft-core atoms to demonstrate how higher-order dispersive effects induce isotopic, isomeric, and Zeeman-like spectral variations in their resonance frequencies.
This paper introduces HMC, a hybrid algorithm combining Exact Diagonalization and Hamiltonian Monte Carlo that overcomes the scaling limits of ED and the sign problem of standard Monte Carlo methods to enable efficient simulations of larger 2D arrays of coupled quantum wires.
This paper introduces a reduced-order modeling technique for nonlinear radiative transfer in high-energy density physics that combines Proper Orthogonal Decomposition with Galerkin projection on the Boltzmann transport equation to generate closures for low-order quasidiffusion and material energy balance systems, demonstrating their accuracy through numerical results.
This paper introduces multilevel iterative schemes that combine parallel multigroup and grey low-order Second-Moment equations with Anderson acceleration to efficiently solve multigroup Boltzmann transport equations.
This paper proposes implicit approximation methods for time-dependent thermal radiative transfer that utilize Proper Orthogonal Decomposition (POD) to approximate high-dimensional radiation intensity data, thereby significantly reducing memory storage requirements while maintaining solution accuracy.
This paper introduces a multilevel iteration method based on a nonlinear projection approach and a V-cycle algorithm to solve linear particle transport problems in binary stochastic mixtures by coupling high-order transport equations with low-order Yvon-Mertens and quasidiffusion equations.
This paper presents a nonlinear projection-based multilevel iterative scheme that performs cycles over multiple time steps by alternating between the high-order Boltzmann transport equation and low-order moment equations with exact Eddington closure, effectively transforming fully implicit temporal integrators into diagonally-implicit multi-step schemes for efficiently simulating thermal radiative transfer problems.