326 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 demonstrates that coverage-oriented uncertainty quantification methods, which integrate uncertainty directly into the optimization process, outperform post-hoc techniques in modeling the complex, multi-regime physical behaviors of Critical Heat Flux by producing models with both high predictive accuracy and physically consistent uncertainty estimates.
This paper presents a Bayesian framework that leverages historical network snapshots to establish informative priors for predicting future network configurations and quantifying uncertainty, demonstrating superior out-of-sample reconstruction accuracy on interbank market data compared to existing link prediction benchmarks.
This paper presents a Bayesian multi-wavelength imaging algorithm based on information field theory to denoise, deconvolve, and decompose eROSITA X-ray data, successfully applying it to SN1987A in the Large Magellanic Cloud to reveal fine-scale structures and identify point sources.
This review presents the first unified conceptual framework for scientific machine learning in hydrology, organizing fragmented methodological families into a coherent structure to clarify novelty, foster cumulative progress, and guide future research directions.
This paper demonstrates that applying Gaussian low-pass filtering to high-dimensional molecular dynamics coordinates effectively mitigates projection artifacts, thereby recovering hidden conformational states and yielding more accurate, long-lived, and structurally defined free energy landscapes.
This paper introduces improved versions of Event Synchronization (ES) and Event Coincidence Analysis (ECA) to better handle normalization and boundary issues, demonstrating through numerical and real-world applications that while ES is sensitive to serial dependency and event clustering, ECA serves as a more robust method for analyzing synchronization in event time series.
This paper proposes a resource-efficient complex-valued autoregressive model that transforms solar irradiance forecasts by incorporating volatility as an imaginary component, demonstrating superior or comparable accuracy to classical methods while offering broad applicability across various scientific fields.
This paper establishes a rigorous benchmarking framework for solar radiation forecasting by evaluating five naive reference methods, including a newly proposed ARTU model and their ensemble combination, to demonstrate that selecting the most appropriate reference method based on specific data characteristics and forecast horizons is essential for fair performance assessment.
This paper presents a novel sparsity-based texture tomography method that reconstructs high-resolution orientation distribution functions in anisotropic polycrystalline samples without peak-finding, enabling the mapping of complex microstructures in materials like shot-peened martensite and gastropod shells that are difficult to analyze with existing techniques.
This paper proposes a novel "Clearsky-Free" short-term solar radiation forecasting approach using Extreme Learning Machines (ELM) that directly learns from raw Global Horizontal Irradiance data to eliminate the limitations and complexities of traditional clearsky models while achieving accuracy and robust uncertainty quantification comparable to or better than existing benchmarks.