1164 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 an asymptotic gauge-invariant Hybrid High-Order method for magnetic Schrödinger equations that utilizes a discrete covariant gradient on arbitrary polyhedral meshes to ensure gauge covariance, stability, and optimal convergence, as validated by numerical experiments reproducing key quantum phenomena.
This paper demonstrates that turbulent circulation fluctuations in homogeneous and isotropic turbulence can be accurately modeled using a superstatistical framework based on q-exponentials, linking the dissipation field to small-scale vortices and opening new avenues for understanding turbulence through non-extensive statistical mechanics.
This study presents a computationally efficient semi-analytical Timoshenko beam model on a Winkler foundation to predict the transverse vibration and dynamic amplification of buried lifelines, revealing four distinct vibration regimes separated by transition frequencies and validated against finite element simulations.
This paper introduces a lightweight, fully connected neural network framework trained on synthetic Monte Carlo datasets to accurately estimate three-dimensional, spatially resolved scatter radiation fields for interventional radiology dosimetry, achieving high spatial agreement and a consistent SMAPE above 84% while providing open-source datasets and tools.
This paper introduces AeTHERON, a physics-informed heterogeneous graph neural operator that leverages a dual-graph architecture and sparse cross-attention to efficiently and accurately model complex fluid-structure interactions in flapping flexible fins, achieving high-fidelity temporal extrapolation with significantly reduced computational costs compared to traditional direct numerical simulations.
This paper proposes and validates an improved third-order scheme for pseudopotential lattice Boltzmann models that effectively suppresses spurious velocity oscillations at phase interfaces without adding computational complexity, thereby ensuring more reliable simulations of multiphase flows.
This paper presents a transferable coarse-grained model for sodium dodecyl sulfate (SDS) in water based on the MDPD–Martini force field, which successfully reproduces experimental surface tension isotherms and offers a credible alternative to traditional MD–Martini simulations for charged soft-matter systems.
This paper demonstrates that a ResNet-based machine learning framework (WatChMaL) can achieve particle classification and kinematic reconstruction for Hyper-Kamiokande events with accuracy comparable to traditional methods while offering a massive speed-up of over 30,000 times, thereby enabling the processing of the experiment's required large-scale Monte Carlo datasets.
This paper presents a surrogate-accelerated hierarchical Bayesian calibration framework that successfully derives data-informed dissipative particle dynamics models for commercial ultrasound contrast agents by inferring their mechanical parameters from force spectroscopy data across multiple bubble diameters.