327 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.
This paper presents a platform-agnostic quantum reservoir computing framework utilizing a small-scale six-qubit system to achieve over 86% accuracy in forecasting stock trends for quantum-sector companies, demonstrating the potential of near-term quantum hardware for complex financial time-series analysis.
This paper proposes a general Simulation-Based Inference framework utilizing Factorizable Normalizing Flows and an amortized training strategy to efficiently profile systematic uncertainties while simultaneously extracting multivariate distributions of interest, overcoming the computational bottlenecks of traditional unbinned likelihood fits.
This paper proposes a Bayesian sequential approach for time-lapse Full Waveform Inversion using Hamiltonian Monte Carlo to effectively quantify uncertainties in high-dimensional seismic problems by integrating baseline survey data as prior knowledge, demonstrating accuracy comparable to parallel schemes while managing computational costs.
This paper demonstrates that a small, strategically selected subset of experimental data—a "textbook"—derived from over 1,000 gust-wing interaction events can train machine learning models with accuracy comparable to much larger random datasets, thereby distilling essential physical information to enhance modeling efficiency and interpretability.
This paper demonstrates that wind farm power fluctuations are driven by universal, nonlinear, and collective correlations—specifically a dynamical scaling transition and long-range non-Gaussian features—which amplify variability and intermittency in total power output.
This paper derives the stationary distributions of an extended Chiarella financial model across different regimes, identifying specific conditions for unimodal and bimodal distributions and correcting previous misconceptions regarding the relationship between trend and mispricing bifurcations.
This paper proposes a methodology to infer unknown parameters and reconstruct hidden macroscopic dynamics of finite-size neural networks by synchronizing a known mean-field model with a single scalar observable using a differential evolution algorithm.
The paper presents a versatile method for generating customizable multifractal fields based on the Multifractal Random Walk model and demonstrates its utility by "unwrapping" experimental data from a fractured alloy to reveal structural differences between synthetic and real-world multifractals.
By analyzing a massive dataset of electronic invoices from Ceará, Brazil, this study uses community detection, revealed comparative advantage, and Maximum Entropy Models to demonstrate that inter-city trade networks form cohesive, modular structures that operate near a "critical point" of economic stability.
To address the inability of linear Exponential Random Graphs (ERGs) to account for degree distribution variance, this paper proposes a "softened" fitness-induced variant of the two-star model that successfully reproduces both the mean and variance of empirical degree distributions within a canonical framework.