1241 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 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 ab initio study reveals that nickel atoms diffuse significantly slower than iron atoms in Fe-Ni alloys due to a coupling between local lattice distortions and spin-polarized electronic structure, which causes iron to relax into vacancies while nickel remains rigidly fixed.
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 introduces a framework for designing new density functionals that achieve unprecedented accuracy and balanced performance for transition-metal heterogeneous catalysis, specifically reaching chemical accuracy for adsorption energies and correcting qualitative failures of standard functionals while remaining computationally efficient and open-source.
This paper introduces an Engineering-Oriented Symbolic Regression framework that leverages Large Language Models as physics-informed agents to autonomously discover simulation-ready, thermodynamically consistent constitutive laws for complex materials, successfully overcoming the numerical instability of traditional models while achieving high predictive accuracy.
This paper presents a fully open-source framework that enables quasi-real-time streaming of low-field MRI data to the cloud for advanced reconstruction and post-processing, effectively overcoming the computational limitations of portable MRI consoles while preserving system affordability and portability.
This study employs large-scale molecular dynamics simulations to demonstrate that two-dimensional titanium-based MXene metamaterials with atomically defined perforations exhibit tunable auxetic behavior driven by a rotating-junction mechanism coupled with out-of-plane deflections, offering a versatile platform for designing 2D mechanical metamaterials.
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 theoretically investigates energy renormalizations in electrostatically-doped WSe monolayers under strong magnetic fields, demonstrating that dynamical screening significantly affects resident carriers while exchange interactions explain the surprisingly weak energy shifts observed in tightly bound excitons.
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.