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 introduces a novel machine learning framework featuring NAC-specific descriptors and a phase-correction procedure that achieves unprecedented accuracy () in predicting nonadiabatic coupling vectors, enabling robust and efficient fully ML-driven fewest-switches surface hopping simulations for photochemical processes.
This paper introduces the SPARSE algorithm, which employs the finite difference method to numerically solve radial Schrödinger equations for inelastic scattering of particles with spin, enabling the calculation of physical K-matrices, scattering poles, and amplitudes.
This paper resolves the Blanc-Cances instability of the Wang-Teter nonlocal kinetic energy density functional in isolated systems by introducing a density-functional-dependent kernel, thereby achieving an order-of-magnitude accuracy improvement for single atoms while maintaining superior performance in bulk metals.
This paper presents a consistent kinetic modeling and discretization strategy using a double-distribution quasi-equilibrium approach that enables accurate, stable, and Galilean-invariant simulations of compressible flows across all Prandtl numbers and specific heat ratios, successfully recovering Navier-Stokes-Fourier physics for both moderate supersonic speeds and complex discontinuities.
This paper proposes a stable differentiable modal synthesis framework that combines scalar auxiliary variable techniques with neural ordinary differential equations to learn nonlinear dynamics, enabling direct physical parameter interpretation and demonstrating success in modeling the nonlinear transverse vibration of a string.
This paper proposes a Hadamard regularization-based separation-of-scales ansatz within the Schwinger-Keldysh formalism to develop a computationally efficient time-stepping algorithm for the Kadanoff-Baym equations, enabling the simulation of damped quantum harmonic oscillators in unstructured environments while capturing non-Markovian and renormalization effects without prohibitive cubic scaling.
This paper introduces TT-Metadynamics, a scalable method that compresses the bias potential in metadynamics into a low-rank tensor train representation using a sketching algorithm, thereby enabling efficient free energy exploration in high-dimensional systems with up to 14 collective variables without the exponential computational cost of standard approaches.
By training an autoregressive conditional diffusion model on Kolmogorov flow simulations, this study reveals a state-dependent hierarchy in the predictability of turbulence extremes, demonstrating that forecast horizons are governed by the persistence of large-scale coherent structures and the lifetime of pre-extreme strain cores.
This paper introduces a weighted graph model (FIU) where information-driven topology evolution governed by spectral curvature exhibits a sharp phase transition at a critical coupling strength, revealing a stable discrete Poisson relation between curvature and information flux that spontaneously demonstrates dimensional sensitivity through distinct system-size collapse thresholds in 2D versus 3D lattices.
This paper presents a 3D topology optimization framework for designing manufacturable blazed metasurface gratings that achieve high broadband diffraction efficiency in the visible and near-infrared spectrum by transitioning from complex freeform structures to fabrication-constrained pillar-based parameterizations compatible with e-beam lithography and reactive ion etching.