1258 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.
DRIFT-Net is a novel dual-branch neural operator that integrates a spectral branch for global low-frequency coupling with an image branch for local details, effectively mitigating error accumulation and drift in PDE learning while achieving superior accuracy, efficiency, and parameter economy compared to state-of-the-art attention-based baselines.
This paper proposes a novel all-altermagnetic tunnel junction paradigm using a RuO2/NiF2/RuO2 structure that achieves ultra-high tunneling magnetoresistance and spin filtering efficiencies by leveraging the synergistic momentum-dependent spin splitting of altermagnets, thereby combining the benefits of zero stray fields and low power consumption with high-performance spintronic functionality.
This paper presents an all-electron framework implemented in the open-source code QCDark2 that incorporates random-phase approximation dielectric screening with local field effects to accurately predict dark matter-electron scattering rates across various target materials, demonstrating that these effects significantly modify recoil spectra and detection sensitivities for both non-relativistic and boosted dark matter.
This paper proposes a Bayesian optimal experimental design framework, enhanced by Gaussian and surrogate approximations, to optimize specimen geometries and loading paths for reliably identifying parameters in history-dependent constitutive models while minimizing experimental costs.
This paper introduces a Proper Orthogonal Decomposition-based reduced-order framework that significantly accelerates the Variational Deterministic-Particle-Based Scheme for 3D Fokker-Planck equations in polymer dynamics, achieving a 94% reduction in computational time with minimal accuracy loss for complex multi-bead systems.
This paper establishes that Single-Layer Physics-Informed Neural Networks suffer from dual optimization pathologies—baseline and compounding failures driven by spectral bias and PDE nonlinearity—that prevent error reduction with increased width, revealing that optimization, rather than approximation capacity, is the primary bottleneck governed by complex, non-separable scaling laws.
This paper introduces a versatile, GPU-accelerated neural network framework that utilizes an adaptive Metropolis-Adjusted Langevin Algorithm to accurately solve diverse quantum few-body systems with improved energy precision, robust convergence, and enhanced physical insight compared to previous methods.
Through first-principles calculations and machine learning, this study reveals a previously unrecognized hydrogen-atom roaming reaction mechanism in water clusters, identifying the reactant dipole moment as the key factor governing this unique pathway that complements the fundamental understanding of water reactivity.
This paper presents advancements in manufacturing ultra-broadband optical multilayer filters with over 100 thin layers using improved RF magnetron sputtering techniques, specifically deposition duration control and in situ optical reflectance monitoring, which were optimized through cross-studies of Nb2O5, TiO2, and SiO2 refractive indexes.
This paper investigates how the interplay between self-propulsion and velocity alignment, particularly under boundary friction, dictates distinct accumulation patterns of active agents, ranging from delocalized states to compact clusters, thereby offering a framework to infer dominant microscopic interactions in collective behaviors.