1148 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 novel thin-sheet volume integral equation solver that rigorously enforces generalized sheet transition conditions for 3D bianisotropic metasurfaces by treating tangential and normal flux densities as distinct unknowns, thereby improving accuracy over conventional methods while demonstrating robust performance in various electromagnetic transformation scenarios.
This paper introduces Pi-PINN, a transferable learning framework that leverages a shared physics-informed embedding space and closed-form pseudoinverse head adaptation to solve diverse partial differential equations rapidly and accurately with minimal or no training data for unseen instances.
This study demonstrates that applying in-plane equibiaxial tensile strain to the room-temperature altermagnet KVSeO eliminates parasitic Fermi pockets, thereby restoring flat-band geometry and achieving a record charge-to-spin conversion efficiency of approximately 96%, which establishes strain engineering as a practical route for high-efficiency spintronic devices.
This paper proposes an agentic AI framework that integrates large language models with lightweight physics-based models to autonomously evaluate and optimize thermal comfort and building energy performance in tropical urban environments through rapid, closed-loop reasoning and action.
This paper presents a meshless -adaptive numerical method for simulating non-Newtonian natural convection in a differentially heated cavity, demonstrating that dynamically adjusting node density based on shear-thinning flow characteristics significantly enhances computational efficiency while accurately capturing sharp boundary layer structures.
This study proposes a Hierarchical Physically Recurrent Neural Network (HPRNN) framework that integrates micromechanical data and physical laws across two scales to create a computationally efficient, interpretable, and generalizable surrogate model for predicting the nonlinear elasto-plastic behavior of woven composites under complex cyclic loading.
This study investigates the finite-temperature phase diagram of two-dimensional soft-core bosons using self-consistent Hartree-Fock and quantum Monte Carlo methods, identifying a broad supersolid phase and a potential intermediate hexatic phase while validating mean-field theory as an effective tool for analyzing these transitions.
This paper demonstrates that in spin-network quantum reservoir computing, reservoirs exhibiting finite quantum entanglement and coherence maintain greater stability and performance under statistical noise compared to their classical counterparts, suggesting that practical measurement constraints may actually favor quantum-enhanced regimes.
This study demonstrates that the choice of electron-positron correlation functional, particularly the use of the non-local weighted density approximation (WDA), is critical for accurately predicting positron annihilation lifetimes in lead halide perovskites, revealing that previous discrepancies in theoretical predictions and experimental interpretations of cation vacancies stem from the sensitivity of these materials to the specific approximation used.
By restoring the exact algebra of the screw dislocation group to establish rigorous symmetry constraints, this study reveals that a piezoelectric effect at the core of screw dislocations in GaN strongly suppresses radiative recombination in favor of non-radiative capture, thereby explaining their detrimental impact on the material's luminous efficiency.