1253 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 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.
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 study proposes a robust methodology utilizing polynomial machine learning potentials to systematically enumerate crystal structures and evaluate phase stability for elemental silicon under high-pressure (up to 100 GPa) and finite-temperature (up to 1000 K) conditions.
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.