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 study proposes a synergistic framework combining causality-informed training with residual-based adaptive refinement to significantly enhance the accuracy and efficiency of Physics-Informed Neural Networks in solving spatio-temporal PDEs with complex, evolving interfaces, as demonstrated by improved performance in Allen-Cahn phase field modeling.
This paper proposes a universal reweight-annealing scheme that resolves the general measurement problem in Quantum Monte Carlo simulations by expressing target observables as ratios of partition functions, thereby enabling the calculation of diverse correlations and disorder operators across various quantum models and dimensions while offering broader applications in statistical data analysis.
This paper presents a unified framework for calculating electrostatic energy and pressure in periodic Coulomb systems by introducing infinite boundary terms and effective pairwise interactions, which allow the treatment of both neutral and non-neutral systems with point charges and continuous charge distributions using a formulation analogous to isolated systems.
This study presents the first *ab initio* calculation of the nuclear Schiff moment for the F isotope using the no-core shell model, which, when combined with quantum chemistry calculations and experimental data on HfF, establishes the first experimental bound on this moment and its associated pion-nucleon-nucleon coupling constants.
The paper proposes a variational boundary-based tensor network renormalization group algorithm that utilizes globally optimized boundary tensors to construct accurate renormalization projectors, achieving higher precision than existing methods while maintaining the computational complexity of the original TRG approach.
This study demonstrates that active matter systems, particularly those forming liquid droplets, serve as robust reservoirs for computing by utilizing nonlinear driving forces to trigger emergent structural and dynamic features that enhance information processing performance regardless of the specific injection method.
This paper introduces NextHAM, a universal deep learning framework combining a novel E(3)-symmetric Transformer architecture and a zeroth-step Hamiltonian correction strategy, alongside a large-scale benchmark dataset (Materials-HAM-SOC), to achieve highly accurate and efficient prediction of electronic-structure Hamiltonians across diverse materials while explicitly accounting for spin-orbit coupling effects.
This study combines numerical simulations and experimental analysis to evaluate the mechanical stability of commercial and modified intraocular lenses, revealing that minor geometric changes in haptic design significantly affect performance and identifying the V4 model as the optimal structure for minimizing decentration and enhancing patient comfort.
This paper presents an experimentally validated, interpretable workflow that uses integrated gradients on a lightweight surrogate model to generate pixel-level sensitivity maps for inverse-designed photonic devices, effectively identifying physically meaningful high-sensitivity regions that guide fabrication-aware design optimization.
This paper presents a novel, computationally efficient nonlinear unsteady aerodynamic reduced-order model that integrates oscillator dynamics with Volterra theory to accurately reproduce complex aeroelastic shock buffet phenomena, such as lock-in, in three-dimensional transonic flows.