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 unified machine learning framework that combines machine-learned interatomic potentials with neural classical density functional theory to enable efficient, first-principles multiscale modeling of liquid thermodynamics and phase behavior across homogeneous and inhomogeneous systems.
This paper proposes a closed-form conditional diffusion model for data assimilation that leverages kernel density estimation to analytically compute the score function, enabling efficient black-box state estimation for nonlinear, non-Gaussian systems that outperforms traditional ensemble Kalman and particle filters with small to moderate ensemble sizes.
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 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 demonstrates through experiments and modeling that symmetric CoFeB/Ru/CoFeB synthetic antiferromagnets in a scissors state exhibit strong frequency non-reciprocity in their acoustical spin waves, enabling unidirectional energy transfer for wavevectors parallel to the applied magnetic field.
This study investigates the coupling between surface acoustic waves and ferromagnetic resonance in a CoFeB film on a LiNbO3 substrate, revealing an unexpected 2-fold symmetry in SAW energy absorption that is attributed to weak in-plane uniaxial anisotropy rather than dipolar interactions.
This paper demonstrates that machine learning-based virtual sensing can accurately estimate solder layer degradation and reconstruct full temperature maps in IGBT modules using limited physical sensors, achieving a mean absolute error of 1.17% for degraded area and a maximum relative error of 4.56% for surface temperature.
This paper introduces FESTIM v2.0, a major upgrade to the open-source finite element framework for hydrogen transport that enhances physical capabilities through multi-species support and advanced reaction schemes while improving performance and sustainability via migration to the DOLFINx platform.
This paper investigates the observational signatures of magnetically charged Anti-de Sitter black holes in string-inspired Euler-Heisenberg theory, demonstrating that magnetic charge alters photon trajectories, orbital frequencies, and the innermost stable circular orbit, while twin-peak quasi-periodic oscillation data constrain the magnetic charge parameter to be less than approximately 0.2 times the black hole mass at the 1 sigma confidence level.
This paper introduces the Neural Uncertainty Principle (NUP) to unify adversarial fragility in vision and hallucination in language models as a shared geometric phenomenon governed by an input-gradient uncertainty bound, enabling the development of efficient, training-free methods like ConjMask and LogitReg to enhance model robustness and detect hallucinations.