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 utilizes a hybrid dynamical systems framework to analyze the Spring-Loaded Inverted Pendulum (SLIP) model, identifying stability regions and demonstrating how to exploit unstable dynamics for constant-energy gait transitions while achieving near-universal stability through simple non-constant angle of attack control policies.
This paper identifies that SCAN and r2SCAN functionals struggle with non-compact covalent bonds due to biased electron localization descriptions and proposes the r2SCAN+V approach as a practical solution that significantly improves accuracy across challenging materials like graphene, Fe, Cr₂, and VO₂.
This paper proposes and simulates a scalable spin-qubit architecture in pristine graphene p-n junctions, where strain-induced nanobubbles create tunable double quantum dots that enable coherent spin manipulation via Rashba spin-orbit coupling and Zeeman fields, as evidenced by distinct avoided crossings and detuning-dependent Rabi oscillations.
This paper demonstrates that optimizing training data continuity and employing a specialized loss function enables deep learning models to achieve robust, transferable satellite-derived bathymetry across diverse coastal regions, significantly outperforming traditional machine learning baselines and existing benchmarks while leveraging multi-temporal imagery to mitigate environmental noise.
This paper introduces TransportBench, a comprehensive high-fidelity dataset and standardized benchmark designed to evaluate and diagnose scientific machine learning models across diverse non-equilibrium flow regimes, revealing that no single neural architecture universally outperforms others and that specific inductive biases are required for different flow characteristics.
This paper introduces PaNO, a propagation-aligned neural operator that prioritizes output-port readout fidelity over global field accuracy to prevent neural field surrogates from misguiding photonic device design, particularly in propagation-dominated structures like MMI splitters.
This paper proposes a variable-coefficient model for the - turbulence framework that accounts for finite cascade time and Reynolds number effects, thereby accurately capturing the decay and growth of isotropic turbulence across diverse flow scenarios.
This study employs the squirmer model and dissipative particle dynamics to investigate how swimmer shape, volume fraction, hydrodynamic interactions, and rotlet dipoles influence the collective behaviors—ranging from gas-like phases to swarming and motility-induced phase separation—of bacteria in confined thin films, revealing asymmetric structural formation and the mitigating effect of rotlet dipoles on differences between swimmer types.
This paper introduces an iterative method to analytically calculate arbitrary moments of the spectral measure for advection-diffusion in periodic flow fields, enabling the derivation of rigorous, high-order bounds on effective transport that accurately capture known behaviors in 2D steady flows and extend to 3D and time-periodic regimes.
This paper investigates using multilayer perceptrons to accelerate high-order mesh-free methods by either surrogating kernels or solving associated linear systems, finding that while the latter approach achieves significant speedups with high accuracy, it faces fundamental challenges as higher-order approximations impose increasingly stringent requirements on the neural network's predictive precision.