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 presents a comprehensive phase-field fracture model for brittle materials under thermal shock that independently specifies bulk properties, strength, and toughness, successfully unifying the prediction of diverse fracture scenarios—from crack propagation to nucleation and branching—across various experimental cases where classical approaches fall short.
This paper presents a computationally efficient, two-dimensional analytical model for characterizing toroidal magnetic rings with circular cross-sections under sinusoidal excitation, deriving explicit expressions for internal fields, impedance, and separated loss components to serve as an accurate alternative to finite element analysis for standardized material testing.
This paper introduces a trainable photonic quantum neural field that optimizes optical phases and photon-number measurements as a representation for physics-informed PDE learning, demonstrating superior accuracy and parameter efficiency over classical baselines in complex regimes where residual derivatives amplify phase mismatches.
This paper presents an iterative AI framework that autonomously proposes, evaluates, and refines dynamical dark-energy equations of state, successfully identifying novel phenomenological parameterizations that outperform traditional models in Bayesian evidence when tested against cosmological observations.
This paper proposes a novel curvature measure derived from the heat kernel expansion as a sensitive indicator for detecting financial shocks and legislative impacts in Norwegian corporate networks, demonstrating its superiority over traditional topological metrics like the Euler characteristic and torsion in capturing local network variations.
This paper presents the first systematic scan of two-flavon Froggatt-Nielsen models using the Non-dominated Sorting Genetic Algorithm III to efficiently identify over 100,000 phenomenologically viable configurations that naturally explain CP violation, fermion masses, and mixing patterns across both quark and lepton sectors.
This study demonstrates that DFT-trained MACE neural network potentials accurately reproduce the structural, dynamic, and kinetic properties of aqueous magnesium solutions, including water-exchange mechanisms, but currently fail to quantitatively capture solvation free energies due to limitations in modeling long-range electrostatic effects.
This paper presents an extended anisotropic social force model that incorporates realistic body shapes, physical contact forces, and behavioral factors like panic and herding to simulate a big-box store evacuation, revealing that restricting egress to main entrances while ignoring staff exits significantly increases evacuation times.
This paper proposes a computational framework combining a Liouville-space adjoint formulation, Jacobian-based updates, and simulated annealing to optimize the multi-objective design of photon blockade, achieving high success rates in balancing purity, brightness, and indistinguishability for bright single-photon sources without relying on analytical guidance.
This paper demonstrates that a four-wing chaotic attractor in a dissipative system arises from the intersection of two energy-like Hamiltonian functions and can be analytically localized within a finite phase space region using Nambu mechanics without numerical simulation.