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.
DRIFT-Net is a novel dual-branch neural operator that integrates a spectral branch for global low-frequency coupling with an image branch for local details, effectively mitigating error accumulation and drift in PDE learning while achieving superior accuracy, efficiency, and parameter economy compared to state-of-the-art attention-based baselines.
This paper establishes that Single-Layer Physics-Informed Neural Networks suffer from dual optimization pathologies—baseline and compounding failures driven by spectral bias and PDE nonlinearity—that prevent error reduction with increased width, revealing that optimization, rather than approximation capacity, is the primary bottleneck governed by complex, non-separable scaling laws.
This paper presents the first comprehensive full-f drift-kinetic and -f gyrokinetic simulations of the LAPD linear plasma device, revealing that ion distributions are approximately bi-Maxwellian and that Kelvin-Helmholtz-driven turbulence dominates the system, with small-scale structures only significantly amplified under reduced collisionality and enhanced source conditions.
The paper introduces QMatSuite, an open-source platform that enables AI agents in computational materials science to consolidate experimental knowledge through provenance tracking and reflection, significantly reducing reasoning overhead and improving accuracy across both familiar and unfamiliar material simulations.
This paper establishes a rigorous theoretical framework and proposes an adaptive sampling algorithm for robust mode tracking in single-parametric Hermitian eigenvalue problems, specifically addressing challenges in SAFE dispersion analysis such as mode veering and degeneracy through novel error indicators and symmetry-aware subspace metrics.
This paper proposes a singular-value-decomposition-free method for constructing initial tensor networks and demonstrates that while Tensor Renormalization Group (TRG) results depend heavily on these initial tensors, this dependence can be eliminated and numerical robustness significantly improved by employing the boundary TRG technique.
This paper proposes and demonstrates a method using tensor network states within the Hamiltonian formalism to accurately compute parton distribution functions for the vector meson in the massive Schwinger model directly in Minkowski space, thereby overcoming the limitations of Euclidean lattice calculations and offering a pathway for quantum simulations.
This study predicts that hole doping reduces the coercive field in ferroelectric hafnia from 8 MV/cm to 6 MV/cm by activating a lower-energy polarization switching pathway through the Pbcm phase, thereby transforming the material from an improper to a proper ferroelectric.
This paper introduces the Spin-MInt algorithm, the first rigorously symplectic and time-reversible propagator designed to directly simulate nonadiabatic dynamics using spin-mapping variables, demonstrating superior accuracy and computational efficiency compared to existing methods across various models.
This paper demonstrates that differentiable programming, enabled by automatic differentiation, serves as a versatile framework in plasma physics that not only accelerates traditional design and inference tasks but also enables novel capabilities such as discovering new nonlinear phenomena, learning hidden kinetic variables for fluid models, and performing high-dimensional inverse design.