1222 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 demonstrates that a Fourier neural operator can serve as an accurate and computationally efficient learned forward operator for solving the inverse problem in photoacoustic tomography via gradient-based optimization, outperforming conventional pseudospectral -space methods in efficiency while maintaining high accuracy.
This paper introduces SPINONet, a neuroscience-inspired, energy-efficient neural operator framework that utilizes sparse, event-driven spiking neurons to solve partial differential equations in computational mechanics while maintaining the continuous differentiability required for physics-informed training.
This study demonstrates that coherent resonant excitation using two copropagating laser pulses with a specific temporal separation of approximately a quarter plasma wavelength can amplify wakefield amplitudes up to three times greater than those generated by a single pulse in homogeneous plasma.
This paper presents a unified heterogeneous computing framework within the ABACUS package that accelerates real-time time-dependent density functional theory simulations based on numerical atomic orbitals through co-designed abstraction layers, demonstrating significant speedups on single GPUs and high parallel efficiency across multiple GPUs for large-scale electron dynamics studies.
This paper presents a systematic method combining exact domain mappings, Theory of Functional Connections (TFC), and transfinite interpolations to enforce Dirichlet, Neumann, and Robin boundary conditions with machine-precision accuracy on general quadrilateral domains with curved boundaries, while rigorously addressing compatibility constraints at boundary intersections.
This paper introduces AMELI, a Python package that utilizes exact arithmetic and a direct Slater determinant approach to efficiently generate universally applicable, high-precision angular matrix elements for lanthanide ions, thereby simplifying the analysis of their radiative spectra for experimentalists.
This paper proposes a robust, automated method combining Fourier analysis and statistical classification to reliably identify and distinguish chimera states from other dynamical patterns in complex coupled oscillator systems, demonstrating its effectiveness and generalizability across varying network topologies and parameters.
This paper introduces a low-cost, intermittent sub-grid correction method using differentiated Riemann variables and a single Newton update to recover machine-precision accuracy for 1D Euler computations on coarse grids, effectively resolving long-standing challenges in capturing sharp contact and shock waves in extreme scenarios like the LeBlanc problem.
This paper introduces a systematic framework based on tangent equations of motion (TEOM) that efficiently computes high-order nonlinear response functions directly from real-time dynamics, overcoming the factorial scaling and numerical instability of traditional methods by isolating perturbative orders through a closed hierarchy of derivative equations.
This paper presents a robust and efficient density matrix-based Kohn-Sham inversion method formulated entirely within a Gaussian basis that utilizes Lowdin orthogonalization and analytical penalty potentials to overcome the limitations of conventional approaches, achieving superior accuracy in reproducing target electron densities for both open-shell molecular and condensed phase systems.