903 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 the Ray-Column IPRM, a structure-based turbulence model that restores radial spectral scale information by projecting conditional states onto finite wavenumber bands, thereby enabling more accurate closure evaluations and the formation of filtered observables compared to traditional orientation-only approaches.
This study demonstrates that using two-color Laguerre-Gaussian laser pulses to drive plasma wakefields fundamentally alters their topology by redistributing longitudinal field energy off-axis into hollow, ring-shaped structures, thereby offering new mechanisms for controlling transverse plasma dynamics and enabling off-axis particle acceleration.
This paper proposes a Physics Informed Neural Network (PINN)-based computational method that directly solves for time-periodic flow states by optimizing over a single period rather than simulating transient initial conditions, thereby achieving significant reductions in computational time while maintaining accuracy comparable to traditional mesh-based solvers.
This paper presents a numerically stable Method of Moments tool for simulating S-parameters of multilayer boxed PCBs by combining an S-matrix formalism to derive the complete dyadic Green's function with various basis functions to model both transverse and longitudinal currents.
Using variational Monte Carlo methods with neural quantum states, this study analyzes the near-kernel sector of the Hamiltonian constraint in quantum reduced loop gravity and identifies three distinct classes of solutions, including a factorized branch that is accurately described by emergent semiclassical Thiemann coherent states.
This paper presents a novel MPI-based method for constructing and simulating large-scale spiking neural networks on multi-GPU clusters and exascale supercomputers, demonstrating efficient scaling for complex cortical models through optimized local connectivity and spike exchange strategies.
This paper introduces the Symplectic Neural Operator, a novel architecture that preserves the intrinsic symplectic structure of infinite-dimensional Hamiltonian systems to ensure rigorous long-term stability and improved energy conservation compared to standard data-driven models.
The paper introduces the Quantum Feature Amplification Network (QFAN), an autoregressive quantum generative model that overcomes the register-size bottleneck in calorimeter shower simulation by generating images as sequences of blocks using a fixed-size quantum circuit, successfully demonstrating its ability to reproduce key physical distributions on both simulators and IBM quantum hardware.
This paper presents a highly efficient, multi-GPU-accelerated implementation of the Discontinuous Galerkin ocean model SLIM that achieves massive speedups over CPU-based systems and enables ultra-high-resolution coastal simulations, such as a five-fold resolution improvement for the Great Barrier Reef.
This paper demonstrates how combining a generative AI coding agent with an LLM-driven tree search algorithm can autonomously discover optimized three-dimensional photovoltaic structures, provided the system iteratively patches physics constraints to eliminate algorithmic reward hacking and ensure physically valid solutions.