261 papers
This section explores the intersection where physics meets data analysis, a rapidly evolving frontier where complex datasets reveal hidden patterns in the universe. From tracking particle collisions to modeling cosmic structures, these studies rely on advanced statistical methods to turn raw numbers into fundamental insights about how reality works.
Gist.Science monitors every new preprint in this category as it appears on arXiv, ensuring you never miss a breakthrough. We process each entry to provide both plain-language overviews for general understanding and detailed technical summaries for experts, bridging the gap between dense research and clear comprehension.
Below are the latest papers in physics and data analysis, organized for easy reading and discovery.
FcsIT is a new, open-source, cross-platform Python tool that utilizes the Dear PyGUI engine to provide robust, user-friendly correlation, filtering, and flexible model-fitting capabilities for analyzing Fluorescence Correlation Spectroscopy data with quality comparable to commercial software.
This paper introduces new analytical tools demonstrating that deviations from normality in stationary growth-rate distributions are natural statistical outcomes rather than anomalies, proposing a generalized logistic distribution as a robust null model and a practical workflow for identifying macroecological patterns in systems with limited data quality.
This paper presents a Bayesian physics-informed neural network approach for flow field tomography that leverages governing fluid dynamics equations to reconstruct 2D flows from sparse line-of-sight measurements while providing comprehensive uncertainty quantification to address semi-convergence issues caused by noise.
This paper introduces a novel physics-informed background-oriented schlieren (BOS) workflow that utilizes physics-informed neural networks to simultaneously reconstruct accurate density, velocity, and pressure fields in supersonic flows by integrating measurement data with governing Euler and irrotationality equations, thereby overcoming the limitations of conventional methods and achieving the first PINN-based reconstruction of supersonic flow from experimental data.
This paper introduces Stochastic Particle Advection Velocimetry (SPAV), a novel framework utilizing a statistical data loss and physics-informed neural networks to significantly improve the accuracy of particle tracking velocimetry by explicitly modeling particle advection and accounting for localization uncertainties, thereby reducing reconstruction errors by approximately 50% in both simulated and experimental fluid flow measurements.
This paper introduces Neural Optical Flow (NOF), a continuous neural-implicit framework that enhances the accuracy, robustness, and data compression of particle image velocimetry (PIV) by integrating differentiable image warping, physical constraints like Navier-Stokes residuals, and tailored network expressivity to enable advanced analysis of both planar and stereo flows.
This study demonstrates that information dissemination on social networks is not random but follows specific, discernible patterns through global and local preferential selection models, where new content tends to propagate along the same pathways as previous news.
This paper presents two machine-learning approaches for the ATLAS Muon Spectrometer to address High-Luminosity LHC challenges: a Graph Neural Network that improves background-hit rejection and reconstruction speed by 15%, and a Vision Transformer proof-of-concept achieving 98% tracking efficiency in just 2.3 ms.
This paper proposes a Bayesian framework using mixtures of Gaussian process experts to flexibly model absorption spectra, statistically integrate Kramers-Kronig relations with error-aware anchoring, and automatically select measurement points for robustly estimating the complex refractive index of materials like gallium arsenide, potassium chloride, and transparent wood.
Using bias-corrected 20CRv2c reanalysis data from 1876 to 2011, this study demonstrates that human-caused climate change, primarily through rising temperatures and decreasing humidity, has driven a statistically significant increase in the mean and extreme severity of Australian fire weather conditions over the past century.