1259 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 introduces a data-driven framework that extracts wavelength-dependent pseudo-optical constants from minimal transmittance measurements to overcome the characterization limitations of complex, micron-scale photochromic hybrid films, enabling their rational design for adaptive optical applications.
This paper introduces a combined dynamic-kinematic validation framework and a novel diagram to demonstrate that relying solely on maximum spreading diameter is insufficient for accurate droplet impact modeling, advocating instead for a hybrid contact angle model that better captures both geometric spreading limits and internal kinematic receding dynamics.
This paper presents a fluctuating lattice Boltzmann formulation based on orthogonal central moments that introduces stochastic forcing directly in the central moment space to exactly satisfy the fluctuation-dissipation theorem, ensuring thermal equilibrium, Galilean invariance, and enhanced numerical stability even in the over-relaxation regime.
This study combines experimental characterization and crystal plasticity modeling to demonstrate that increasing yttrium content in extruded Mg alloys suppresses common TT1 tension twins while promoting rare TT2 twins, alters critical resolved shear stress ratios, and induces higher local strain accumulation at TT2 sites, thereby offering new insights for alloy design.
The paper introduces PLANCK, a physics-inspired deep reinforcement learning framework leveraging hypergraph neural networks and gauge symmetry to efficiently solve large-scale p-spin models and various NP-hard combinatorial optimization problems with superior zero-shot generalization compared to state-of-the-art methods.
This paper introduces a scalable, convex optimization-based information-matching approach using the Fisher Information Matrix to select optimal training data that specifically constrains parameters relevant to downstream quantities of interest, thereby enabling precise predictions with minimal data across diverse scientific fields and active learning applications.
This paper proposes a novel scale-free network model driven by the competition between degree and betweenness centralities, which reveals a new "stars-with-filament" structural class, a tunable phase diagram encompassing 47 real-world networks, and unique scaling behaviors for average degree and path length.
This paper presents a hierarchical finite-element framework enhanced by sparse operator-adapted wavelet decomposition that decouples resolution levels to enable efficient, nearly linear-complexity analysis of multiscale electromagnetic problems without requiring the recomputation of coarser-level solutions.
The paper introduces LEMONS, an open-source platform featuring Python and C++ libraries along with a graphical interface, designed to generate realistic, non-circular pedestrian shapes based on anthropometric data and simulate their mechanical interactions in two dimensions to overcome the limitations of traditional circular crowd models.
This paper introduces a kinetic closure for the filtered Boltzmann-BGK equation that naturally incorporates turbulent subfilter stresses and diffusion without requiring scale separation or explicit modeling, demonstrating improved stability and reduced dissipation in lattice Boltzmann simulations compared to the Smagorinsky model.